2020.07.07.txt

==========New Papers==========
1, TITLE:       Learning Motion Flows for Semi-supervised Instrument Segmentation from Robotic Surgical Video
http://arxiv.org/abs/2007.02501
AUTHORS:        Zixu Zhao ; Yueming Jin ; Xiaojie Gao ; Qi Dou ; Pheng-Ann Heng
COMMENTS:       Accepted for MICCAI 2020
HIGHLIGHT:      In this paper, we study the semi-supervised instrument segmentation from robotic surgical videos with sparse annotations.

2, TITLE:       You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion
http://arxiv.org/abs/2007.02220
AUTHORS:        Roei Schuster ; Congzheng Song ; Eran Tromer ; Vitaly Shmatikov
HIGHLIGHT:      We demonstrate that neural code autocompleters are vulnerable to data- and model-poisoning attacks.

3, TITLE:       Are Labels Necessary for Classifier Accuracy Evaluation?
http://arxiv.org/abs/2007.02915
AUTHORS:        Weijian Deng ; Liang Zheng
HIGHLIGHT:      Specifically, given a labeled training set and a model, we aim to estimate the model accuracy on unlabeled test datasets.

4, TITLE:       Point-Set Anchors for Object Detection, Instance Segmentation and Pose Estimation
http://arxiv.org/abs/2007.02846
AUTHORS:        Fangyun Wei ; Xiao Sun ; Hongyang Li ; Jingdong Wang ; Stephen Lin
COMMENTS:       To appear in ECCV 2020
HIGHLIGHT:      To facilitate inference, we propose to instead perform regression from a set of points placed at more advantageous positions.

5, TITLE:       MCMI: Multi-Cycle Image Translation with Mutual Information Constraints
http://arxiv.org/abs/2007.02919
AUTHORS:        Xiang Xu ; Megha Nawhal ; Greg Mori ; Manolis Savva
HIGHLIGHT:      We present a mutual information-based framework for unsupervised image-to-image translation.

6, TITLE:       Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning
http://arxiv.org/abs/2007.02832
AUTHORS:        Silviu Pitis ; Harris Chan ; Stephen Zhao ; Bradly Stadie ; Jimmy Ba
COMMENTS:       12 pages (+12 appendix). Published as a conference paper at ICML 2020. Code available at https://github.com/spitis/mrl
HIGHLIGHT:      We propose to optimize this objective by having the agent pursue past achieved goals in sparsely explored areas of the goal space, which focuses exploration on the frontier of the achievable goal set.

7, TITLE:       A Survey on Sensor Technologies for Unmanned Ground Vehicles
http://arxiv.org/abs/2007.01992
AUTHORS:        Qi Liu ; Shihua Yuan ; Zirui Li
COMMENTS:       9 pages,6 tables
HIGHLIGHT:      This paper proposes a brief review on sensor technologies for UGVs.

8, TITLE:       INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving
http://arxiv.org/abs/2007.02924
AUTHORS:        Yuhuai Wu ; Albert Jiang ; Jimmy Ba ; Roger Grosse
HIGHLIGHT:      In this paper, we introduce INT, an INequality Theorem proving benchmark, specifically designed to test agents' generalization ability.

9, TITLE:       A Family of Denominator Bounds for First Order Linear Recurrence Systems
http://arxiv.org/abs/2007.02926
AUTHORS:        Mark van Hoeij ; Moulay Barkatou ; Johannes Middeke
COMMENTS:       13 pages
HIGHLIGHT:      For linear recurrence systems, the problem of finding rational solutions is reduced to the problem of computing polynomial solutions by computing a content bound or a denominator bound.

10, TITLE:      Bidirectional Model-based Policy Optimization
http://arxiv.org/abs/2007.01995
AUTHORS:        Hang Lai ; Jian Shen ; Weinan Zhang ; Yong Yu
COMMENTS:       Accepted at ICML2020
HIGHLIGHT:      We develop a novel method, called Bidirectional Model-based Policy Optimization (BMPO) to utilize both the forward model and backward model to generate short branched rollouts for policy optimization.

11, TITLE:      BBS-Net: RGB-D Salient Object Detection with a Bifurcated Backbone Strategy Network
http://arxiv.org/abs/2007.02713
AUTHORS:        Fan Deng-Ping ; Zhai Yingjie ; Borji Ali ; Yang Jufeng ; Shao Ling
COMMENTS:       Accepted in ECCV2020. Code: https://github.com/DengPingFan/BBS-Net
HIGHLIGHT:      In this paper, we make the first attempt to leverage the inherent multi-modal and multi-level nature of RGB-D salient object detection to develop a novel cascaded refinement network.

12, TITLE:      Image Stitching Based on Planar Region Consensus
http://arxiv.org/abs/2007.02722
AUTHORS:        Aocheng Li ; Jie Guo ; Yanwen Guo
COMMENTS:       15 pages, 9 figures
HIGHLIGHT:      In this paper, noticing the importance of planar structure under perspective geometry, we propose a new image stitching method which stitches images by allowing for the alignment of a set of matched dominant planar regions.

13, TITLE:      Reward Machines for Cooperative Multi-Agent Reinforcement Learning
http://arxiv.org/abs/2007.01962
AUTHORS:        Cyrus Neary ; Zhe Xu ; Bo Wu ; Ufuk Topcu
HIGHLIGHT:      We accordingly propose a decentralized q-learning algorithm.

14, TITLE:      Complex Human Action Recognition in Live Videos Using Hybrid FR-DL Method
http://arxiv.org/abs/2007.02811
AUTHORS:        Fatemeh Serpush ; Mahdi Rezaei
HIGHLIGHT:      In this paper, we address challenges of the preprocessing phase, by an automated selection of representative frames among the input sequences.

15, TITLE:      Structure-Aware Human-Action Generation
http://arxiv.org/abs/2007.01971
AUTHORS:        Ping Yu ; Yang Zhao ; Chunyuan Li ; Changyou Chen
COMMENTS:       accepted by ECCV 2020
HIGHLIGHT:      To overcome this issue, we propose a variant of GCNs to leverage the powerful self-attention mechanism to adaptively sparsify a complete action graph in the temporal space.

16, TITLE:      Solving Bayesian Network Structure Learning Problem with Integer Linear Programming
http://arxiv.org/abs/2007.02829
AUTHORS:        Ronald Seoh
HIGHLIGHT:      We review the definition and key properties of Bayesian network and explain score metrics used to measure how well certain Bayesian network structure fits the dataset.

17, TITLE:      Lale: Consistent Automated Machine Learning
http://arxiv.org/abs/2007.01977
AUTHORS:        Guillaume Baudart ; Martin Hirzel ; Kiran Kate ; Parikshit Ram ; Avraham Shinnar
COMMENTS:       KDD Workshop on Automation in Machine Learning (AutoML@KDD), August 2020
HIGHLIGHT:      This paper introduces Lale, a library of high-level Python interfaces that simplifies and unifies automated machine learning in a consistent way.

18, TITLE:      Scaling Imitation Learning in Minecraft
http://arxiv.org/abs/2007.02701
AUTHORS:        Artemij Amiranashvili ; Nicolai Dorka ; Wolfram Burgard ; Vladlen Koltun ; Thomas Brox
HIGHLIGHT:      We apply imitation learning to attain state-of-the-art performance on hard exploration problems in the Minecraft environment.

19, TITLE:      Interpretation of Disease Evidence for Medical Images Using Adversarial Deformation Fields
http://arxiv.org/abs/2007.01975
AUTHORS:        Ricardo Bigolin Lanfredi ; Joyce D. Schroeder ; Clement Vachet ; Tolga Tasdizen
COMMENTS:       Accepted for MICCAI 2020
HIGHLIGHT:      We propose a novel method for formulating and presenting spatial explanations of disease evidence, called deformation field interpretation with generative adversarial networks (DeFI-GAN).

20, TITLE:      Dynamic Awareness
http://arxiv.org/abs/2007.02823
AUTHORS:        Joseph Y. Halpern ; Evan Piermont
COMMENTS:       To appear in the 17th International Conference on Principles of Knowledge Representation and Reasoning
HIGHLIGHT:      We investigate how to model the beliefs of an agent who becomes more aware.

21, TITLE:      Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation
http://arxiv.org/abs/2007.01947
AUTHORS:        Guolei Sun ; Wenguan Wang ; Jifeng Dai ; Luc Van Gool
COMMENTS:       Full version of ECCV2020 Oral, CVPR2020 LID workshop Best Paper and LID challenge Track1 winner; website: https://github.com/GuoleiSun/MCIS_wsss
HIGHLIGHT:      This paper studies the problem of learning semantic segmentation from image-level supervision only.

22, TITLE:      Multigrid for Bundle Adjustment
http://arxiv.org/abs/2007.01941
AUTHORS:        Tristan Konolige ; Jed Brown
HIGHLIGHT:      We present an unsmoothed aggregation multigrid preconditioner that accurately represents the global modes that underlie poor scaling of existing methods and demonstrate solves of up to 13 times faster than the state of the art on large, challenging problem sets.

23, TITLE:      Feedback Neural Network based Super-resolution of DEM for generating high fidelity features
http://arxiv.org/abs/2007.01940
AUTHORS:        Ashish Kubade ; Avinash Sharma ; K S Rajan
COMMENTS:       Accepted for publication in IEEE IGARSS 2020 conference
HIGHLIGHT:      Motivated from feedback neural networks, we propose a novel neural network architecture that learns to add high frequency details iteratively to low resolution DEM, turning it into a high resolution DEM without compromising its fidelity.

24, TITLE:      A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images
http://arxiv.org/abs/2007.02180
AUTHORS:        Issam Laradji ; Pau Rodriguez ; Oscar Manas ; Keegan Lensink ; Marco Law ; Lironne Kurzman ; William Parker ; David Vazquez ; Derek Nowrouzezahrai
HIGHLIGHT:      We propose LOOC, a method to Localize Overlapping Objects with Count supervision.

25, TITLE:      BézierSketch: A generative model for scalable vector sketches
http://arxiv.org/abs/2007.02190
AUTHORS:        Ayan Das ; Yongxin Yang ; Timothy Hospedales ; Tao Xiang ; Yi-Zhe Song
COMMENTS:       To be appeared in ECCV 2020
HIGHLIGHT:      In this paper we present B\'ezierSketch, a novel generative model for fully vector sketches that are automatically scalable and high-resolution.

26, TITLE:      Birds of a Feather Flock Together: Satirical News Detection via Language Model Differentiation
http://arxiv.org/abs/2007.02164
AUTHORS:        Yigeng Zhang ; Fan Yang ; Yifan Zhang ; Eduard Dragut ; Arjun Mukherjee
COMMENTS:       10 pages
HIGHLIGHT:      In this work, we propose a method that differentiates the satirical news and true news.

27, TITLE:      Multi-Sensor Next-Best-View Planning as Matroid-Constrained Submodular Maximization
http://arxiv.org/abs/2007.02084
AUTHORS:        Mikko Lauri ; Joni Pajarinen ; Jan Peters ; Simone Frintrop
COMMENTS:       8 pages, 7 figures. Accepted for publication in IEEE Robotics and Automation Letters
HIGHLIGHT:      In this paper, we address next-best-view planning for multiple depth cameras.

28, TITLE:      Local Grid Rendering Networks for 3D Object Detection in Point Clouds
http://arxiv.org/abs/2007.02099
AUTHORS:        Jianan Li ; Jiashi Feng
HIGHLIGHT:      In this work, we aim to improve performance of point-based models by enhancing their pattern learning ability through leveraging CNNs while preserving computational efficiency.

29, TITLE:      Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge
http://arxiv.org/abs/2007.02096
AUTHORS:        Yue Sun ; Kun Gao ; Zhengwang Wu ; Zhihao Lei ; Ying Wei ; Jun Ma ; Xiaoping Yang ; Xue Feng ; Li Zhao ; Trung Le Phan ; Jitae Shin ; Tao Zhong ; Yu Zhang ; Lequan Yu ; Caizi Li ; Ramesh Basnet ; M. Omair Ahmad ; M. N. S. Swamy ; Wenao Ma ; Qi Dou ; Toan Duc Bui ; Camilo Bermudez Noguera ; Bennett Landman ; Ian H. Gotlib ; Kathryn L. Humphreys ; Sarah Shultz ; Longchuan Li ; Sijie Niu ; Weili Lin ; Valerie Jewells ; Gang Li ; Dinggang Shen ; Li Wang
HIGHLIGHT:      To promote methodological development in the community, iSeg-2019 challenge (http://iseg2019.web.unc.edu) provides a set of 6-month infant subjects from multiple sites with different protocols/scanners for the participating methods.

30, TITLE:      End-to-end Learning of a Fisher Vector Encoding for Part Features in Fine-grained Recognition
http://arxiv.org/abs/2007.02080
AUTHORS:        Dimitri Korsch ; Paul Bodesheim ; Joachim Denzler
HIGHLIGHT:      Therefore, we propose integrating a Fisher vector encoding of part features into convolutional neural networks.

31, TITLE:      Customized Handling of Unintended Interface Operation in Assistive Robots
http://arxiv.org/abs/2007.02092
AUTHORS:        Deepak Gopinath ; Mahdieh Nejati Javaremi ; Brenna D. Argall
COMMENTS:       12 pages, 8 figures, preprint
HIGHLIGHT:      In this paper, we present an assistance system that reasons about a human's intended actions during robot teleoperation in order to provide appropriate corrections for unintended behavior.

32, TITLE:      Novel-View Human Action Synthesis
http://arxiv.org/abs/2007.02808
AUTHORS:        Mohamed Ilyes Lakhal ; Davide Boscaini ; Fabio Poiesi ; Oswald Lanz ; Andrea Cavallaro
HIGHLIGHT:      Our approach uses a novel 3D reasoning to synthesize the target viewpoint.

33, TITLE:      Efficient and accurate object detection with simultaneous classification and tracking
http://arxiv.org/abs/2007.02065
AUTHORS:        Xuesong Li ; Jose Guivant
HIGHLIGHT:      To satisfy both requirements, we propose a detection framework based on simultaneous classification and tracking in the point stream.

34, TITLE:      Weight-dependent Gates for Network Pruning
http://arxiv.org/abs/2007.02066
AUTHORS:        Yun Li ; Weiqun Wu ; Zechun Liu ; Chi Zhang ; Xiangyu Zhang ; Haotian Yao ; Baoqun Yin
COMMENTS:       15 pages, 5 figures
HIGHLIGHT:      In this paper, we propose a simple and effective network pruning framework, which introduces novel weight-dependent gates (W-Gates) to prune filter adaptively.

35, TITLE:      El Departamento de Nosotros: How Machine Translated Corpora Affects Language Models in MRC Tasks
http://arxiv.org/abs/2007.01955
AUTHORS:        Maria Khvalchik ; Mikhail Galkin
HIGHLIGHT:      In this work, we study the caveats of applying directly translated corpora for fine-tuning LMs for downstream natural language processing tasks and demonstrate that careful curation along with post-processing lead to improved performance and overall LMs robustness.

36, TITLE:      Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation
http://arxiv.org/abs/2007.01951
AUTHORS:        Liwei Wang ; Jing Huang ; Yin Li ; Kun Xu ; Zhengyuan Yang ; Dong Yu
HIGHLIGHT:      To address this challenge, we leverage a generic object detector at training time, and propose a contrastive learning framework that accounts for both region-phrase and image-sentence matching.

37, TITLE:      Registration of Histopathogy Images Using Structural Information From Fine Grained Feature Maps
http://arxiv.org/abs/2007.02078
AUTHORS:        Dwarikanath Mahapatra
HIGHLIGHT:      We propose a novel approach of combining segmentation information in a registration framework using self supervised segmentation feature maps extracted using a pre-trained segmentation network followed by clustering.

38, TITLE:      Quo Vadis, Skeleton Action Recognition ?
http://arxiv.org/abs/2007.02072
AUTHORS:        Pranay Gupta ; Anirudh Thatipelli ; Aditya Aggarwal ; Shubh Maheshwari ; Neel Trivedi ; Sourav Das ; Ravi Kiran Sarvadevabhatla
COMMENTS:       Reference video : https://www.youtube.com/watch?v=YKjQcV_2gLU
HIGHLIGHT:      In this paper, we study current and upcoming frontiers across the landscape of skeleton-based human action recognition.

39, TITLE:      Human-Robot Team Coordination with Dynamic and Latent Human Task Proficiencies: Scheduling with Learning Curves
http://arxiv.org/abs/2007.01921
AUTHORS:        Ruisen Liu Manisha Natarajan Matthew Gombolay
HIGHLIGHT:      We introduce a novel resource coordination algorithm that enables robots to explore the relative strengths and learning abilities of their human teammates, by constructing schedules that are robust to stochastic and time-varying human task performance.

40, TITLE:      Knowledge Distillation Beyond Model Compression
http://arxiv.org/abs/2007.01922
AUTHORS:        Fahad Sarfraz ; Elahe Arani ; Bahram Zonooz
COMMENTS:       Accepted at ICPR 2020
HIGHLIGHT:      In this study, we provide an extensive study on nine different KD methods which covers a broad spectrum of approaches to capture and transfer knowledge.

41, TITLE:      Proving Non-Inclusion of Büchi Automata based on Monte Carlo Sampling
http://arxiv.org/abs/2007.02282
AUTHORS:        Yong Li ; Andrea Turrini ; Andrea Turrini ; Lijun Zhang
COMMENTS:       Accepted to ATVA 2020
HIGHLIGHT:      In this paper, we present $\mathsf{IMC}^2$, a specific algorithm for proving B\"uchi automata non-inclusion $\mathcal{L}(\mathcal{A}) \not\subseteq \mathcal{L}(\mathcal{B})$, based on Grosu and Smolka's algorithm $\mathsf{MC}^2$ developed for Monte Carlo model checking against LTL formulas.

42, TITLE:      TilinGNN: Learning to Tile with Self-Supervised Graph Neural Network
http://arxiv.org/abs/2007.02278
AUTHORS:        Hao Xu ; Ka Hei Hui ; Chi-Wing Fu ; Hao Zhang
COMMENTS:       SIGGRAPH 2020, Technical paper. ACM Trans. Graph., Vol. 39, No. 4, Article 129. Homapage: https://appsrv.cse.cuhk.edu.hk/~haoxu/projects/TilinGnn/index.html
HIGHLIGHT:      We introduce the first neural optimization framework to solve a classical instance of the tiling problem.

43, TITLE:      Weakly Supervised Domain Adaptation for Built-up Region Segmentation in Aerial and Satellite Imagery
http://arxiv.org/abs/2007.02277
AUTHORS:        Javed Iqbal ; Mohsen Ali
COMMENTS:       Accepted at ISPRS Journal of Photogrammetry and Remote Sensing
HIGHLIGHT:      This paper proposes a novel domain adaptation algorithm to handle the challenges posed by the satellite and aerial imagery, and demonstrates its effectiveness on the built-up region segmentation problem.

44, TITLE:      Multi view stereo with semantic priors
http://arxiv.org/abs/2007.02295
AUTHORS:        Elisavet Konstantina Stathopoulou ; Fabio Remondino
HIGHLIGHT:      In this study, we aim to support the standard dense 3D reconstruction of scenes as implemented in the open source library OpenMVS by using semantic priors.

45, TITLE:      Graph2Kernel Grid-LSTM: A Multi-Cued Model for Pedestrian Trajectory Prediction by Learning Adaptive Neighborhoods
http://arxiv.org/abs/2007.01915
AUTHORS:        Sirin Haddad ; Siew Kei Lam
HIGHLIGHT:      We present a new perspective to interaction modeling by proposing that pedestrian neighborhoods can become adaptive in design.

46, TITLE:      Abstractive and mixed summarization for long-single documents
http://arxiv.org/abs/2007.01918
AUTHORS:        Roger Barrull ; Jugal Kalita
HIGHLIGHT:      In this work, six different models are compared, two with an RNN architecture, one with a CNN architecture, two with a Transformer architecture and one with a Transformer architecture combined with reinforcement learning.

47, TITLE:      EmotionGIF-Yankee: A Sentiment Classifier with Robust Model Based Ensemble Methods
http://arxiv.org/abs/2007.02259
AUTHORS:        Wei-Yao Wang ; Kai-Shiang Chang ; Yu-Chien Tang
COMMENTS:       EmotionGIF 2020, the shared task of SocialNLP 2020
HIGHLIGHT:      This paper provides a method to classify sentiment with robust model based ensemble methods.

48, TITLE:      Automatically Generating Codes from Graphical Screenshots Based on Deep Autocoder
http://arxiv.org/abs/2007.02272
AUTHORS:        Xiaoling Huang ; Feng Liao
HIGHLIGHT:      To solve this problem, we propose PixCoder based on an artificially supervised attention mechanism.

49, TITLE:      Rethinking Bottleneck Structure for Efficient Mobile Network Design
http://arxiv.org/abs/2007.02269
AUTHORS:        Zhou Daquan ; Qibin Hou ; Yunpeng Chen ; Jiashi Feng ; Shuicheng Yan
COMMENTS:       24 pages, published as a ECCV20 conference paper
HIGHLIGHT:      In this paper, we rethink the necessity of such design changes and find it may bring risks of information loss and gradient confusion.

50, TITLE:      Image Aesthetics Prediction Using Multiple Patches Preserving the Original Aspect Ratio of Contents
http://arxiv.org/abs/2007.02268
AUTHORS:        Lijie Wang ; Xueting Wang ; Toshihiko Yamasaki
HIGHLIGHT:      We propose a multi-patch method, named MPA-Net (Multi-Patch Aggregation Network), to predict image aesthetics scores by maintaining the original aspect ratios of contents in the images.

51, TITLE:      Deep Learning based Dimple Detection for Quantitative Fractography
http://arxiv.org/abs/2007.02267
AUTHORS:        Ashish Sinha ; KS Suresh
COMMENTS:       7 pages, 8 figs
HIGHLIGHT:      In this work, we try to address the challenging problem of dimple detection and segmentation in Titanium alloys using machine learning methods, especially neural networks.

52, TITLE:      A Systematic Evaluation of Object Detection Networks for Scientific Plots
http://arxiv.org/abs/2007.02240
AUTHORS:        Pritha Ganguly ; Nitesh Methani ; Mitesh M. Khapra ; Pratyush Kumar
HIGHLIGHT:      Given this poor performance, we propose minor modifications to existing models by combining ideas from different object detection networks.

53, TITLE:      News Sentiment Analysis
http://arxiv.org/abs/2007.02238
AUTHORS:        Antony Samuels ; John Mcgonical
HIGHLIGHT:      This paper presents a lexicon-based approach for sentiment analysis of news articles.

54, TITLE:      Sentiment Analysis on Customer Responses
http://arxiv.org/abs/2007.02237
AUTHORS:        Antony Samuels ; John Mcgonical
HIGHLIGHT:      We present a customer feedback reviews on product, where we utilize opinion mining, text mining and sentiments, which has affected the surrounded world by changing their opinion on a specific product.

55, TITLE:      Stereo Visual Inertial Pose Estimation Based on Feedforward-Feedback Loops
http://arxiv.org/abs/2007.02250
AUTHORS:        Shengyang Chen ; Chih-Yung Wen ; Yajing Zou ; Wu Chen
COMMENTS:       14 pages, 14 figures, 2 tables
HIGHLIGHT:      In this paper, we present a novel stereo visual inertial pose estimation method.

56, TITLE:      Spatial-Angular Attention Network for Light Field Reconstruction
http://arxiv.org/abs/2007.02252
AUTHORS:        Gaochang Wu ; Yebin Liu ; Lu Fang ; Tianyou Chai
COMMENTS:       12 pages, 10 figures and 5 tables
HIGHLIGHT:      In this paper, we propose a spatial-angular attention network to perceive correspondences in the light field non-locally, and reconstruction high angular resolution light field in an end-to-end manner.

57, TITLE:      Face Anti-Spoofing with Human Material Perception
http://arxiv.org/abs/2007.02157
AUTHORS:        Zitong Yu ; Xiaobai Li ; Xuesong Niu ; Jingang Shi ; Guoying Zhao
COMMENTS:       Accepted by ECCV2020
HIGHLIGHT:      In this paper we rephrase face anti-spoofing as a material recognition problem and combine it with classical human material perception [1], intending to extract discriminative and robust features for FAS.

58, TITLE:      Blind Inverse Gamma Correction with Maximized Differential Entropy
http://arxiv.org/abs/2007.02246
AUTHORS:        Yong Lee ; Shaohua Zhang ; Miao Li ; Xiaoyu He
COMMENTS:       12 pages, 8 figures
HIGHLIGHT:      For blind inverse gamma correction, an adaptive gamma transformation method (AGT-ME) is proposed directly from a maximized differential entropy model.

59, TITLE:      Neuro-Symbolic Generative Art: A Preliminary Study
http://arxiv.org/abs/2007.02171
AUTHORS:        Gunjan Aggarwal ; Devi Parikh
COMMENTS:       Accepted as a short paper at ICCC 2020
HIGHLIGHT:      In this work, we propose a new hybrid genre: neuro-symbolic generative art.

60, TITLE:      Unsupervised Paraphrasing via Deep Reinforcement Learning
http://arxiv.org/abs/2007.02244
AUTHORS:        A. B. Siddique ; Samet Oymak ; Vagelis Hristidis
HIGHLIGHT:      We propose Progressive Unsupervised Paraphrasing (PUP): a novel unsupervised paraphrase generation method based on deep reinforcement learning (DRL).

61, TITLE:      Sentiment Analysis on Social Media Content
http://arxiv.org/abs/2007.02144
AUTHORS:        Antony Samuels ; John Mcgonicle
HIGHLIGHT:      The aim of this paper is to present a model that can perform sentiment analysis of real data collected from Twitter.

62, TITLE:      On Class Orderings for Incremental Learning
http://arxiv.org/abs/2007.02145
AUTHORS:        Marc Masana ; Bartłomiej Twardowski ; Joost van de Weijer
COMMENTS:       Accepted at CL-ICML 2020. First two authors contributed equally
HIGHLIGHT:      In this paper, we investigate the impact of class orderings for incrementally learned classifiers.

63, TITLE:      An Integer Approximation Method for Discrete Sinusoidal Transforms
http://arxiv.org/abs/2007.02232
AUTHORS:        R. J. Cintra
COMMENTS:       13 pages, 5 figures, 8 tables
HIGHLIGHT:      In this work, we propose and analyze a class of integer transforms for the discrete Fourier, Hartley, and cosine transforms (DFT, DHT, and DCT), based on simple dyadic rational approximation methods.

64, TITLE:      Human Assisted Artificial Intelligence Based Technique to Create Natural Features for OpenStreetMap
http://arxiv.org/abs/2007.02149
AUTHORS:        Piyush Yadav ; Dipto Sarkar Shailesh Deshpande ; Edward Curry
COMMENTS:       3 pages, 2 Figures, Submitted to FOSS4G Europe 2020 Academic Track (Postponed to 2021)
HIGHLIGHT:      In this work, we propose an AI-based technique using freely available satellite images like Landsat and Sentinel to create natural features over OSM in congruence with human editors acting as initiators and validators.

65, TITLE:      Reflection-based Word Attribute Transfer
http://arxiv.org/abs/2007.02598
AUTHORS:        Yoichi Ishibashi ; Katsuhito Sudoh ; Koichiro Yoshino ; Satoshi Nakamura
COMMENTS:       Accepted at ACL 2020 Student Research Workshop (SRW)
HIGHLIGHT:      In this work, we propose a novel method for word attribute transfer based on reflection mappings without such an analogy operation.

66, TITLE:      A Novel Multi-Step Finite-State Automaton for Arbitrarily Deterministic Tsetlin Machine Learning
http://arxiv.org/abs/2007.02114
AUTHORS:        K. Darshana Abeyrathna ; Ole-Christoffer Granmo ; Rishad Shafik ; Alex Yakovlev ; Adrian Wheeldon ; Jie Lei ; Morten Goodwin
COMMENTS:       10 pages, 8 figures, 7 tables
HIGHLIGHT:      In this paper, we propose a novel finite-state learning automaton that can replace the Tsetlin Automata in TM learning, for increased determinism.

67, TITLE:      Playing Chess with Limited Look Ahead
http://arxiv.org/abs/2007.02130
AUTHORS:        Arman Maesumi
COMMENTS:       11 pages, 7 figures
HIGHLIGHT:      We have seen numerous machine learning methods tackle the game of chess over the years.

68, TITLE:      A Case for Lifetime Reliability-Aware Neuromorphic Computing
http://arxiv.org/abs/2007.02210
AUTHORS:        Shihao Song ; Anup Das
COMMENTS:       4 pages, 6 figures, accepted at MWCAS 2020
HIGHLIGHT:      In this work, we evaluate the long-term, i.e., lifetime reliability impact of executing state-of-the-art machine learning tasks on a neuromorphic hardware, considering failure models such as negative bias temperature instability (NBTI) and time-dependent dielectric breakdown (TDDB).

69, TITLE:      On Connections between Regularizations for Improving DNN Robustness
http://arxiv.org/abs/2007.02209
AUTHORS:        Yiwen Guo ; Long Chen ; Yurong Chen ; Changshui Zhang
COMMENTS:       Accepted by TPAMI
HIGHLIGHT:      This paper analyzes regularization terms proposed recently for improving the adversarial robustness of deep neural networks (DNNs), from a theoretical point of view.

70, TITLE:      Deep Graph Random Process for Relational-Thinking-Based Speech Recognition
http://arxiv.org/abs/2007.02126
AUTHORS:        Hengguan Huang ; Fuzhao Xue ; Hao Wang ; Ye Wang
COMMENTS:       Accepted at ICML 2020
HIGHLIGHT:      In this paper, we present a Bayesian nonparametric deep learning method called deep graph random process (DGP) that can generate an infinite number of probabilistic graphs representing percepts.

71, TITLE:      A Modern Non-SQL Approach to Radiology-Centric Search Engine Design with Clinical Validation
http://arxiv.org/abs/2007.02124
AUTHORS:        Ningcheng Li ; Guy Maresh ; Maxwell Cretcher ; Khashayar Farsad ; Ramsey Al-Hakim ; John Kaufman ; Judy Gichoya
HIGHLIGHT:      We present a de novo process of developing a document-based, secure, efficient, and accurate search engine in the context of Radiology.

72, TITLE:      Meta-Learning through Hebbian Plasticity in Random Networks
http://arxiv.org/abs/2007.02686
AUTHORS:        Elias Najarro ; Sebastian Risi
HIGHLIGHT:      Inspired by this biological mechanism, we propose a search method that, instead of optimizing the weight parameters of neural networks directly, only searches for synapse-specific Hebbian learning rules that allow the network to continuously self-organize its weights during the lifetime of the agent.

73, TITLE:      On the Influence of Ageing on Face Morph Attacks: Vulnerability and Detection
http://arxiv.org/abs/2007.02684
AUTHORS:        Sushma Venkatesh ; Kiran Raja ; Raghavendra Ramachandra ; Christoph Busch
COMMENTS:       Accepted in IJCB 2020
HIGHLIGHT:      In this paper, we report a systematic investigation on the vulnerability of the Commercial-Off-The-Shelf (COTS) FRS when morphed images under the influence of ageing are presented. To this extent, we have introduced a new morphed face dataset with ageing derived from the publicly available MORPH II face dataset, which we refer to as MorphAge dataset.

74, TITLE:      Offline versus Online Triplet Mining based on Extreme Distances of Histopathology Patches
http://arxiv.org/abs/2007.02200
AUTHORS:        Milad Sikaroudi ; Benyamin Ghojogh ; Amir Safarpoor ; Fakhri Karray ; Mark Crowley ; H. R. Tizhoosh
COMMENTS:       The first two authors contributed equally to this work
HIGHLIGHT:      We analyze the effect of offline and online triplet mining for colorectal cancer (CRC) histopathology dataset containing 100,000 patches.

75, TITLE:      Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences
http://arxiv.org/abs/2007.02671
AUTHORS:        Xiangyu Duan ; Baijun Ji ; Hao Jia ; Min Tan ; Min Zhang ; Boxing Chen ; Weihua Luo ; Yue Zhang
COMMENTS:       Accepted by ACL2020
HIGHLIGHT:      In this paper, we propose a new task of machine translation (MT), which is based on no parallel sentences but can refer to a ground-truth bilingual dictionary.

76, TITLE:      Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural Networks
http://arxiv.org/abs/2007.02075
AUTHORS:        Andrea Bordone Molini ; Diego Valsesia ; Giulia Fracastoro ; Enrico Magli
HIGHLIGHT:      In this paper, inspired by recent works on blind-spot denoising networks, we propose a self-supervised Bayesian despeckling method.

77, TITLE:      A Broad-Coverage Deep Semantic Lexicon for Verbs
http://arxiv.org/abs/2007.02670
AUTHORS:        James Allen ; Hannah An ; Ritwik Bose ; Will de Beaumont ; Choh Man Teng
COMMENTS:       Draft of LREC-2020 paper. Proceedings of The 12th Language Resources and Evaluation Conference. 2020
HIGHLIGHT:      We have developed COLLIE-V, a deep lexical resource for verbs, with the coverage of WordNet and syntactic and semantic details that meet or exceed existing resources.

78, TITLE:      Jointly Modeling Motion and Appearance Cues for Robust RGB-T Tracking
http://arxiv.org/abs/2007.02041
AUTHORS:        Pengyu Zhang ; Jie Zhao ; Dong Wang ; Huchuan Lu ; Xiaoyun Yang
COMMENTS:       12 pages, 11 figures
HIGHLIGHT:      In this study, we propose a novel RGB-T tracking framework by jointly modeling both appearance and motion cues.

79, TITLE:      Single Image Brightening via Multi-Scale Exposure Fusion with Hybrid Learning
http://arxiv.org/abs/2007.02042
AUTHORS:        Chaobing Zheng ; Zhengguo Li ; Yi Yang ; Shiqian Wu
COMMENTS:       11 pages
HIGHLIGHT:      In this paper, a single image brightening algorithm is introduced to brighten such an image.

80, TITLE:      An Elastic Interaction-Based Loss Function for Medical Image Segmentation
http://arxiv.org/abs/2007.02663
AUTHORS:        Yuan Lan ; Yang Xiang ; Luchan Zhang
HIGHLIGHT:      Deep learning techniques have shown their success in medical image segmentation since they are easy to manipulate and robust to various types of datasets.

81, TITLE:      Toward unsupervised, multi-object discovery in large-scale image collections
http://arxiv.org/abs/2007.02662
AUTHORS:        Huy V. Vo ; Patrick Pérez ; Jean Ponce
COMMENTS:       Accepted for publication in ECCV 2020
HIGHLIGHT:      We build on the optimization approach of Vo {\em et al.}~\cite{Vo2019UnsupOptim} with several key novelties: (1) We propose a novel saliency-based region proposal algorithm that achieves significantly higher overlap with ground-truth objects than other competitive methods.

82, TITLE:      Self-Calibration Supported Robust Projective Structure-from-Motion
http://arxiv.org/abs/2007.02045
AUTHORS:        Rui Gong ; Danda Pani Paudel ; Ajad Chhatkuli ; Luc Van Gool
COMMENTS:       21 pages, 5 figures, 2 tables
HIGHLIGHT:      In this paper, we propose a unified SfM method, in which the matching process is supported by self-calibration constraints.

83, TITLE:      Discount Factor as a Regularizer in Reinforcement Learning
http://arxiv.org/abs/2007.02040
AUTHORS:        Ron Amit ; Ron Meir ; Kamil Ciosek
COMMENTS:       Published in ICML 2020
HIGHLIGHT:      In this work, we fill in this gap.

84, TITLE:      Separating Positive and Negative Data Examples by Concepts and Formulas: The Case of Restricted Signatures
http://arxiv.org/abs/2007.02669
AUTHORS:        Jean Christoph Jung ; Carsten Lutz ; Hadrien Pulcini ; Frank Wolter
COMMENTS:       35 pages
HIGHLIGHT:      We study the separation of positive and negative data examples in terms of description logic (DL) concepts and formulas of decidable FO fragments, in the presence of an ontology.

85, TITLE:      Modality Shifting Attention Network for Multi-modal Video Question Answering
http://arxiv.org/abs/2007.02036
AUTHORS:        Junyeong Kim ; Minuk Ma ; Trung Pham ; Kyungsu Kim ; Chang D. Yoo
COMMENTS:       CVPR2020 accepted; poster
HIGHLIGHT:      This paper considers a network referred to as Modality Shifting Attention Network (MSAN) for Multimodal Video Question Answering (MVQA) task.

86, TITLE:      Low Rank Fusion based Transformers for Multimodal Sequences
http://arxiv.org/abs/2007.02038
AUTHORS:        Saurav Sahay ; Eda Okur ; Shachi H Kumar ; Lama Nachman
COMMENTS:       ACL 2020 workshop on Second Grand Challenge and Workshop on Multimodal Language
HIGHLIGHT:      In this work, we experiment with modeling modality-specific sensory signals to attend to our latent multimodal emotional intentions and vice versa expressed via low-rank multimodal fusion and multimodal transformers.

87, TITLE:      Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains
http://arxiv.org/abs/2007.02035
AUTHORS:        Quande Liu ; Qi Dou ; Pheng-Ann Heng
COMMENTS:       Early accepted by MICCAI 2020; code and dataset are available at https://github.com/liuquande/SAML
HIGHLIGHT:      We present a novel shape-aware meta-learning scheme to improve the model generalization in prostate MRI segmentation.

88, TITLE:      Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation
http://arxiv.org/abs/2007.02054
AUTHORS:        Jianfeng Zhang ; Xuecheng Nie ; Jiashi Feng
HIGHLIGHT:      In this work, we propose a novel framework, Inference Stage Optimization (ISO), for improving the generalizability of 3D pose models when source and target data come from different pose distributions.

89, TITLE:      Lazy Greedy Hypervolume Subset Selection from Large Candidate Solution Sets
http://arxiv.org/abs/2007.02050
AUTHORS:        Weiyu Chen ; Hisao Ishibuhci ; Ke Shang
COMMENTS:       Accepted by CEC 2020
HIGHLIGHT:      In this paper, we propose a new lazy greedy algorithm exploiting the submodular property of the hypervolume indicator.

90, TITLE:      Relationship between manifold smoothness and adversarial vulnerability in deep learning with local errors
http://arxiv.org/abs/2007.02047
AUTHORS:        Zijian Jiang ; Jianwen Zhou ; Haiping Huang
COMMENTS:       10 pages, 8 figures, comments are welcome
HIGHLIGHT:      Here, we establish a fundamental relationship between geometry of hidden representations (manifold perspective) and the generalization capability of the deep networks.

91, TITLE:      Deep Bilateral Retinex for Low-Light Image Enhancement
http://arxiv.org/abs/2007.02018
AUTHORS:        Jinxiu Liang ; Yong Xu ; Yuhui Quan ; Jingwen Wang ; Haibin Ling ; Hui Ji
COMMENTS:       15 pages
HIGHLIGHT:      Based on the Retinex decomposition of natural images, this paper proposes a deep learning method for low-light image enhancement with a particular focus on handling the measurement noise.

92, TITLE:      Text Data Augmentation: Towards better detection of spear-phishing emails
http://arxiv.org/abs/2007.02033
AUTHORS:        Mehdi Regina ; Maxime Meyer ; Sébastien Goutal
HIGHLIGHT:      Motivated by a business application of Business Email Compromise (BEC) detection, we propose a corpus and task agnostic text augmentation framework combining different methods, utilizing BERT language model, multi-step back-translation and heuristics.

93, TITLE:      FracBits: Mixed Precision Quantization via Fractional Bit-Widths
http://arxiv.org/abs/2007.02017
AUTHORS:        Linjie Yang ; Qing Jin
HIGHLIGHT:      We propose a novel learning-based algorithm to derive mixed precision models end-to-end under target computation constraints and model sizes.

94, TITLE:      Alpha-Refine: Boosting Tracking Performance by Precise Bounding Box Estimation
http://arxiv.org/abs/2007.02024
AUTHORS:        Bin Yan ; Dong Wang ; Huchuan Lu ; Xiaoyun Yang
HIGHLIGHT:      In this work, we propose a novel, flexible and accurate refinement module called Alpha-Refine, which exploits a precise pixel-wise correlation layer together with a spatial-aware non-local layer to fuse features and can predict three complementary outputs: bounding box, corners and mask.

95, TITLE:      Robust Prediction of Punctuation and Truecasingfor Medical ASR
http://arxiv.org/abs/2007.02025
AUTHORS:        Monica Sunkara ; Srikanth Ronanki ; Kalpit Dixit ; Sravan Bodapati ; Katrin Kirchhoff
COMMENTS:       Accepted for ACL NLPMC workshop 2020
HIGHLIGHT:      This paper proposes a conditional joint modeling framework for prediction of punctuation and truecasing using pretrained masked language models such as BERT, BioBERT and RoBERTa.

96, TITLE:      DRDr: Automatic Masking of Exudates and Microaneurysms Caused By Diabetic Retinopathy Using Mask R-CNN and Transfer Learning
http://arxiv.org/abs/2007.02026
AUTHORS:        Farzan Shenavarmasouleh ; Hamid R. Arabnia
COMMENTS:       The 6th International Conference on Health Informatics & Medical Systems (HIMS'20: July 27-30, 2020, USA)
HIGHLIGHT:      This paper addresses the problem of identifying two main types of lesions - Exudates and Microaneurysms - caused by Diabetic Retinopathy (DR) in the eyes of diabetic patients. We create our normalized database out of e-ophtha EX and e-ophtha MA and tweak Mask R-CNN to detect small lesions.

97, TITLE:      Automatic semantic segmentation for prediction of tuberculosis using lens-free microscopy images
http://arxiv.org/abs/2007.02482
AUTHORS:        Dennis Núñez-Fernández ; Lamberto Ballan ; Gabriel Jiménez-Avalos ; Jorge Coronel ; Mirko Zimic
COMMENTS:       ML for Global Health Workshop at ICML 2020
HIGHLIGHT:      Thus, we employ a U-Net network in our collected dataset to perform the automatic segmentation of the TB cords in order to predict tuberculosis.

98, TITLE:      Logic, Language, and Calculus
http://arxiv.org/abs/2007.02484
AUTHORS:        Florian Richter
HIGHLIGHT:      In this paper the difference is examined with regard to inferential relations.

99, TITLE:      Simplicial Complex based Point Correspondence between Images warped onto Manifolds
http://arxiv.org/abs/2007.02381
AUTHORS:        Charu Sharma ; Manohar Kaul
COMMENTS:       Accepted at ECCV 2020
HIGHLIGHT:      In this paper, we pose the assignment problem as finding a bijective map between two graph induced simplicial complexes, which are higher-order analogues of graphs. We propose a constrained quadratic assignment problem (QAP) that matches each p-skeleton of the simplicial complexes, iterating from the highest to the lowest dimension.

100, TITLE:     Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions
http://arxiv.org/abs/2007.02382
AUTHORS:        Michael Chang ; Sidhant Kaushik ; S. Matthew Weinberg ; Thomas L. Griffiths ; Sergey Levine
COMMENTS:       17 pages, 12 figures, accepted to the International Conference on Machine Learning (ICML) 2020
HIGHLIGHT:      This paper seeks to establish a framework for directing a society of simple, specialized, self-interested agents to solve what traditionally are posed as monolithic single-agent sequential decision problems.

101, TITLE:     CIDMP: Completely Interpretable Detection of Malaria Parasite in Red Blood Cells using Lower-dimensional Feature Space
http://arxiv.org/abs/2007.02248
AUTHORS:        Anik Khan ; Kishor Datta Gupta ; Deepak Venugopal ; Nirman Kumar
COMMENTS:       Accepted in The 2020 International Joint Conference on Neural Networks (IJCNN 2020) At Glasgow (UK)
HIGHLIGHT:      To address these issues, in this paper, we propose an approach to extract a very small number of aggregated features that are easy to interpret and compute, and empirically show that we obtain high prediction accuracy even with a significantly reduced feature-space.

102, TITLE:     CORD19STS: COVID-19 Semantic Textual Similarity Dataset
http://arxiv.org/abs/2007.02461
AUTHORS:        Xiao Guo ; Hengameh Mirzaalian ; Ekraam Sabir ; Aysush Jaiswal ; Wael Abd-Almageed
HIGHLIGHT:      To overcome this gap, we introduce CORD19STS dataset which includes 13,710 annotated sentence pairs collected from COVID-19 open research dataset (CORD-19) challenge.

103, TITLE:     MetaConcept: Learn to Abstract via Concept Graph for Weakly-Supervised Few-Shot Learning
http://arxiv.org/abs/2007.02379
AUTHORS:        Baoquan Zhang ; Ka-Cheong Leung ; Yunming Ye ; Xutao Li
COMMENTS:       17 pages, 7 figures
HIGHLIGHT:      To this end, we propose a novel meta-learning framework, called MetaConcept, which learns to abstract concepts via the concept graph.

104, TITLE:     Self-Challenging Improves Cross-Domain Generalization
http://arxiv.org/abs/2007.02454
AUTHORS:        Zeyi Huang ; Haohan Wang ; Eric P. Xing ; Dong Huang
COMMENTS:       to appear at ECCV2020 as an oral paper
HIGHLIGHT:      We introduce a simple training heuristic, Representation Self-Challenging (RSC), that significantly improves the generalization of CNN to the out-of-domain data.

105, TITLE:     Using Capsule Neural Network to predict Tuberculosis in lens-free microscopic images
http://arxiv.org/abs/2007.02457
AUTHORS:        Dennis Núñez-Fernández ; Lamberto Ballan ; Gabriel Jiménez-Avalos ; Jorge Coronel ; Mirko Zimic
COMMENTS:       HSYS Workshop at ICML 2020
HIGHLIGHT:      This work seeks to facilitate and automate the prediction of tuberculosis by the MODS method and using lens-free microscopy, which is easy to use by untrained personnel.

106, TITLE:     Meta-Semi: A Meta-learning Approach for Semi-supervised Learning
http://arxiv.org/abs/2007.02394
AUTHORS:        Yulin Wang ; Jiayi Guo ; Shiji Song ; Gao Huang
HIGHLIGHT:      In this paper, we propose a novel meta-learning based SSL algorithm (Meta-Semi) that requires tuning only one additional hyper-parameter, compared with a standard supervised deep learning algorithm, to achieve competitive performance under various conditions of SSL.

107, TITLE:     Deep Convolutional Neural Network for Identifying Seam-Carving Forgery
http://arxiv.org/abs/2007.02393
AUTHORS:        Seung-Hun Nam ; Wonhyuk Ahn ; In-Jae Yu ; Myung-Joon Kwon ; Minseok Son ; Heung-Kyu Lee
HIGHLIGHT:      In this paper, we propose a convolutional neural network (CNN)-based approach to classifying seam-carving-based image retargeting for reduction and expansion.

108, TITLE:     A modified axiomatic foundation of the analytic hierarchy process
http://arxiv.org/abs/2007.02472
AUTHORS:        Fang Liu ; Wei-Guo Zhang
COMMENTS:       28 pages; 3 figures; submitted to a journal
HIGHLIGHT:      This paper reports a modified axiomatic foundation of the analytic hierarchy process (AHP), where the reciprocal property of paired comparisons is broken.

109, TITLE:     Can Un-trained Neural Networks Compete with Trained Neural Networks at Image Reconstruction?
http://arxiv.org/abs/2007.02471
AUTHORS:        Mohammad Zalbagi Darestani ; Reinhard Heckel
HIGHLIGHT:      To address this question, we consider accelerated magnetic resonance imaging (MRI), an important medical imaging problem, which has received significant attention from the deep-learning community, and for which a dedicated training set exists.

110, TITLE:     Aligning Partially Overlapping Point Sets: an Inner Approximation Algorithm
http://arxiv.org/abs/2007.02363
AUTHORS:        Wei Lian ; WangMeng Zuo ; Lei Zhang
HIGHLIGHT:      To cope with this issue, we employ the inner approximation optimization algorithm which only operates within the region where the objective function is concave.

111, TITLE:     Learning Color Compatibility in Fashion Outfits
http://arxiv.org/abs/2007.02388
AUTHORS:        Heming Zhang ; Xuewen Yang ; Jianchao Tan ; Chi-Hao Wu ; Jue Wang ; C. -C. Jay Kuo
HIGHLIGHT:      Specifically, we model the outfits as graphs and propose a novel graph construction to better utilize the power of graph neural networks.

112, TITLE:     HoughNet: Integrating near and long-range evidence for bottom-up object detection
http://arxiv.org/abs/2007.02355
AUTHORS:        Nermin Samet ; Samet Hicsonmez ; Emre Akbas
COMMENTS:       to appear in ECCV 2020
HIGHLIGHT:      This paper presents HoughNet, a one-stage, anchor-free, voting-based, bottom-up object detection method.

113, TITLE:     Self-supervised Depth Estimation to Regularise Semantic Segmentation in Knee Arthroscopy
http://arxiv.org/abs/2007.02361
AUTHORS:        Fengbei Liu ; Yaqub Jonmohamadi ; Gabriel Maicas ; Ajay K. Pandey ; Gustavo Carneiro
COMMENTS:       10 pages, 6 figures
HIGHLIGHT:      In this paper, we propose a novel self-supervised monocular depth estimation to regularise the training of the semantic segmentation in knee arthroscopy.

114, TITLE:     Estimation of Ground Contacts from Human Gait by a Wearable Inertial Measurement Unit using machine learning
http://arxiv.org/abs/2007.02433
AUTHORS:        Muhammad Junaid Umer ; Qaiser Riaz
HIGHLIGHT:      This article addresses the estimation and classification of right and left foot during the healthy human gait based on the IMU sensor data of chest and lower back. For this purpose we have collected an IMU data of 48 subjects by using two smartphones at chest and lower back of the human body and one smart watch at right ankle of the body.

115, TITLE:     A stochastic calculus approach to the oracle separation of BQP and PH
http://arxiv.org/abs/2007.02431
AUTHORS:        Xinyu Wu
COMMENTS:       6 pages
HIGHLIGHT:      In this short note, we describe such a simplification.

116, TITLE:     GanglionNet: Objectively Assess the Density and Distribution of Ganglion Cells With NABLA-N Network
http://arxiv.org/abs/2007.02367
AUTHORS:        Md Zahangir Alom ; Raj P. Kapur ; TJ Browen ; Vijayan K. Asari
COMMENTS:       8 pages, 8 figures, 2 Tables
HIGHLIGHT:      We present an automated method to detect and count immunostained ganglion cells using a new NABLA_N network based deep learning (DL) approach, called GanglionNet.

117, TITLE:     Detail Preserved Point Cloud Completion via Separated Feature Aggregation
http://arxiv.org/abs/2007.02374
AUTHORS:        Wenxiao Zhang ; Qingan Yan ; Chunxia Xiao
COMMENTS:       To be appeared in ECCV 2020
HIGHLIGHT:      In this work, instead of using a global feature to recover the whole complete surface, we explore the functionality of multi-level features and aggregate different features to represent the known part and the missing part separately.

118, TITLE:     Exploratory Analysis of COVID-19 Related Tweets in North America to Inform Public Health Institutes
http://arxiv.org/abs/2007.02452
AUTHORS:        Hyeju Jang ; Emily Rempel ; Giuseppe Carenini ; Naveed Janjua
HIGHLIGHT:      In this paper, we aim to investigate people's reactions and concerns about COVID-19 in North America, especially focusing on Canada.

119, TITLE:     Auto-captions on GIF: A Large-scale Video-sentence Dataset for Vision-language Pre-training
http://arxiv.org/abs/2007.02375
AUTHORS:        Yingwei Pan ; Yehao Li ; Jianjie Luo ; Jun Xu ; Ting Yao ; Tao Mei
COMMENTS:       The Auto-captions on GIF dataset is available at \url{http://www.auto-video-captions.top/2020/dataset}
HIGHLIGHT:      In this work, we present Auto-captions on GIF, which is a new large-scale pre-training dataset for generic video understanding.

120, TITLE:     Complexity of the Multilevel Critical Node Problem
http://arxiv.org/abs/2007.02370
AUTHORS:        Adel Nabli ; Margarida Carvalho ; Pierre Hosteins
HIGHLIGHT:      In this work, we analyze a sequential game played in a graph called the Multilevel Critical Node problem (MCN).

121, TITLE:     Pynsett: A programmable relation extractor
http://arxiv.org/abs/2007.02100
AUTHORS:        Alberto Cetoli
COMMENTS:       Working paper
HIGHLIGHT:      This paper proposes a programmable relation extraction method for the English language by parsing texts into semantic graphs.

122, TITLE:     Pseudo-Rehearsal for Continual Learning with Normalizing Flows
http://arxiv.org/abs/2007.02443
AUTHORS:        Jary Pomponi ; Simone Scardapane ; Aurelio Uncini
HIGHLIGHT:      In this paper, we propose a novel method that combines the strengths of regularization and generative-based rehearsal approaches.

123, TITLE:     GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis
http://arxiv.org/abs/2007.02442
AUTHORS:        Katja Schwarz ; Yiyi Liao ; Michael Niemeyer ; Andreas Geiger
HIGHLIGHT:      In this paper, we propose a generative model for radiance fields which have recently proven successful for novel view synthesis of a single scene.

124, TITLE:     Anatomical Data Augmentation via Fluid-based Image Registration
http://arxiv.org/abs/2007.02447
AUTHORS:        Zhengyang Shen ; Zhenlin Xu ; Sahin Olut ; Marc Niethammer
COMMENTS:       MICCAI 2020
HIGHLIGHT:      We introduce a fluid-based image augmentation method for medical image analysis.

125, TITLE:     Starfish: A Prototype for Universal Preprocessing and Text-Embedded Programming
http://arxiv.org/abs/2007.02366
AUTHORS:        Vlado Keselj
COMMENTS:       16 pages
HIGHLIGHT:      This paper presents this area of research and related work in a more unified framework, and we describe the implemented system Starfish that satisfies the following novel principles of PTEP: universality, update and replace modes, flexiblity, configurability, and transparency.

126, TITLE:     Attention-based Joint Detection of Object and Semantic Part
http://arxiv.org/abs/2007.02419
AUTHORS:        Keval Morabia ; Jatin Arora ; Tara Vijaykumar
HIGHLIGHT:      In this paper, we address the problem of joint detection of objects like dog and its semantic parts like face, leg, etc.

127, TITLE:     Selective Dyna-style Planning Under Limited Model Capacity
http://arxiv.org/abs/2007.02418
AUTHORS:        Muhammad Zaheer ; Samuel Sokota ; Erin J. Talvitie ; Martha White
COMMENTS:       Accepted at ICML 2020
HIGHLIGHT:      In this paper, we investigate the idea of using an imperfect model selectively.

128, TITLE:     Coronary Heart Disease Diagnosis Based on Improved Ensemble Learning
http://arxiv.org/abs/2007.02895
AUTHORS:        Kuntoro Adi Nugroho ; Noor Akhmad Setiawan ; Teguh Bharata Adji
HIGHLIGHT:      The objective of this study is to develop heart disease diagnosis method based on ensemble learning and cascade generalization.

129, TITLE:     Partial Order Resolution of Event Logs for Process Conformance Checking
http://arxiv.org/abs/2007.02416
AUTHORS:        Han van der Aa ; Henrik Leopold ; Matthias Weidlich
COMMENTS:       Accepted for publication in Decision Support Systems
HIGHLIGHT:      In this paper, we put forward the problem of partial order resolution of event logs to close this gap.

130, TITLE:     Improving Chinese Segmentation-free Word Embedding With Unsupervised Association Measure
http://arxiv.org/abs/2007.02342
AUTHORS:        Yifan Zhang ; Maohua Wang ; Yongjian Huang ; Qianrong Gu
COMMENTS:       9pages, 4figures
HIGHLIGHT:      Recent work on segmentation-free word embedding(sembei) developed a new pipeline of word embedding for unsegmentated language while avoiding segmentation as a preprocessing step.

131, TITLE:     Mission schedule of agile satellites based on Proximal Policy Optimization Algorithm
http://arxiv.org/abs/2007.02352
AUTHORS:        Xinrui Liu
HIGHLIGHT:      In this paper, a mission schedule model combined with Proximal Policy Optimization Algorithm(PPO) is proposed.

132, TITLE:     Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks
http://arxiv.org/abs/2007.02343
AUTHORS:        Yunfei Liu ; Xingjun Ma ; James Bailey ; Feng Lu
COMMENTS:       Accepted by ECCV-2020
HIGHLIGHT:      In this paper, we present a new type of backdoor attack inspired by an important natural phenomenon: reflection.

133, TITLE:     Gradient Origin Networks
http://arxiv.org/abs/2007.02798
AUTHORS:        Sam Bond-Taylor ; Chris G. Willcocks
COMMENTS:       5 pages, 7 figures
HIGHLIGHT:      This paper proposes a new type of implicit generative model that is able to quickly learn a latent representation without an explicit encoder.

134, TITLE:     Universal codes in the shared-randomness model for channels with general distortion capabilities
http://arxiv.org/abs/2007.02330
AUTHORS:        Bruno Bauwens ; Marius Zimand
HIGHLIGHT:      In its standard form, the channel coding problem asks for an encoding function mapping messages to codewords that makes communication over the given channel resilient to a given noise level.

135, TITLE:     Adversarial Uni- and Multi-modal Stream Networks for Multimodal Image Registration
http://arxiv.org/abs/2007.02790
AUTHORS:        Zhe Xu ; Jie Luo ; Jiangpeng Yan ; Ritvik Pulya ; Xiu Li ; William Wells III ; Jayender Jagadeesan
COMMENTS:       accepted by MICCAI 2020
HIGHLIGHT:      In this paper, we propose a novel translation-based unsupervised deformable image registration method.

136, TITLE:     Radial Intersection Count Image: a Clutter Resistant 3D Shape Descriptor
http://arxiv.org/abs/2007.02306
AUTHORS:        Bart Iver van Blokland ; Theoharis Theoharis
COMMENTS:       18 pages, 16 figures, to be published in Computers & Graphics
HIGHLIGHT:      For efficient RICI construction, novel algorithms of general interest were developed.

137, TITLE:     DessiLBI: Exploring Structural Sparsity of Deep Networks via Differential Inclusion Paths
http://arxiv.org/abs/2007.02010
AUTHORS:        Yanwei Fu ; Chen Liu ; Donghao Li ; Xinwei Sun ; Jinshan Zeng ; Yuan Yao
COMMENTS:       conference , 23 pages https://github.com/corwinliu9669/dS2LBI
HIGHLIGHT:      In this paper, instead of pruning or distilling over-parameterized models to compressive ones, we propose a new approach based on differential inclusions of inverse scale spaces.

138, TITLE:     EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning
http://arxiv.org/abs/2007.02491
AUTHORS:        Bailin Li ; Bowen Wu ; Jiang Su ; Guangrun Wang ; Liang Lin
COMMENTS:       Accepted in ECCV 2020(Oral). Codes are available on https://github.com/anonymous47823493/EagleEye
HIGHLIGHT:      In this work, we present a pruning method called EagleEye, in which a simple yet efficient evaluation component based on adaptive batch normalization is applied to unveil a strong correlation between different pruned DNN structures and their final settled accuracy.

139, TITLE:     Inferences and Modal Vocabulary
http://arxiv.org/abs/2007.02487
AUTHORS:        Florian Richter
HIGHLIGHT:      I propose a modal interpretation of implications to express conceptual relations.

140, TITLE:     Space of Reasons and Mathematical Model
http://arxiv.org/abs/2007.02489
AUTHORS:        Florian Richter
HIGHLIGHT:      The conceptual background of representation in models is discussed and in the end I propose how implications of propositional logic and conceptual determinations can be represented in a model of a neural network.

141, TITLE:     Probabilistic Multi-modal Trajectory Prediction with Lane Attention for Autonomous Vehicles
http://arxiv.org/abs/2007.02574
AUTHORS:        Chenxu Luo ; Lin Sun ; Dariush Dabiri ; Alan Yuille
COMMENTS:       IROS 2020. 3rd place solution for the Argoverse motion forecasting challenge at NeurIPS 2019. A variation of the method also won the 1st place in the Nuscenes prediction challenge 2020
HIGHLIGHT:      In this paper, we propose a novel instance-aware representation for lane representation.

142, TITLE:     Progressive Cluster Purification for Unsupervised Feature Learning
http://arxiv.org/abs/2007.02577
AUTHORS:        Yifei Zhang ; Chang Liu ; Yu Zhou ; Wei Wang ; Weiping Wang ; Qixiang Ye
COMMENTS:       8 pages, 5 figures
HIGHLIGHT:      In this work, we propose a novel clustering based method, which, by iteratively excluding class inconsistent samples during progressive cluster formation, alleviates the impact of noise samples in a simple-yet-effective manner.

143, TITLE:     Learning Graph-Convolutional Representations for Point Cloud Denoising
http://arxiv.org/abs/2007.02578
AUTHORS:        Francesca Pistilli ; Giulia Fracastoro ; Diego Valsesia ; Enrico Magli
COMMENTS:       European Conference on Computer Vision (ECCV) 2020
HIGHLIGHT:      We propose a deep neural network based on graph-convolutional layers that can elegantly deal with the permutation-invariance problem encountered by learning-based point cloud processing methods.

144, TITLE:     Learning a Domain Classifier Bank for Unsupervised Adaptive Object Detection
http://arxiv.org/abs/2007.02595
AUTHORS:        Sanli Tang ; Zhanzhan Cheng ; Shiliang Pu ; Dashan Guo ; Yi Niu ; Fei Wu
HIGHLIGHT:      To reduce the gap, recent techniques are proposed by aligning the image/instance-level features between source and unlabeled target domains.

145, TITLE:     SplitFusion: Simultaneous Tracking and Mapping for Non-Rigid Scenes
http://arxiv.org/abs/2007.02108
AUTHORS:        Yang Li ; Tianwei Zhang$ ; Yoshihiko Nakamura ; Tatsuya Harada
COMMENTS:       Accepted to IROS'2020
HIGHLIGHT:      We present SplitFusion, a novel dense RGB-D SLAM framework that simultaneously performs tracking and dense reconstruction for both rigid and non-rigid components of the scene.

146, TITLE:     Discovering Drug-Drug and Drug-Disease Interactions Inducing Acute Kidney Injury Using Deep Rule Forests
http://arxiv.org/abs/2007.02103
AUTHORS:        Bowen Kuo ; Yihuang Kang ; Pinghsung Wu ; Sheng-Tai Huang ; Yajie Huang
HIGHLIGHT:      In this paper, we propose a novel learning algorithm, Deep Rule Forests (DRF), which discovers rules from multilayer tree models as the combinations of drug usages and disease indications to help identify such interactions.

147, TITLE:     On the Connection between Dynamical Optimal Transport and Functional Lifting
http://arxiv.org/abs/2007.02587
AUTHORS:        Thomas Vogt ; Roland Haase ; Danielle Bednarski ; Jan Lellmann
HIGHLIGHT:      In this work, we investigate a mathematically rigorous formulation based on embedding into the space of pointwise probability measures over a fixed range $\Gamma$.

148, TITLE:     Contextual-Relation Consistent Domain Adaptation for Semantic Segmentation
http://arxiv.org/abs/2007.02424
AUTHORS:        Jiaxing Huang ; Shijian Lu ; Dayan Guan ; Xiaobing Zhang
COMMENTS:       Accepted to ECCV 2020
HIGHLIGHT:      This paper presents an innovative local contextual-relation consistent domain adaptation (CrCDA) technique that aims to achieve local-level consistencies during the global-level alignment.

149, TITLE:     Contextualized Spoken Word Representations from Convolutional Autoencoders
http://arxiv.org/abs/2007.02880
AUTHORS:        Prakamya Mishra ; Pranav Mathur
HIGHLIGHT:      This paper proposes a novel model architecture that produces syntactically, contextualized, and semantically adequate representation of varying length spoken words.

150, TITLE:     Automatic Target Recognition on Synthetic Aperture Radar Imagery: A Survey
http://arxiv.org/abs/2007.02106
AUTHORS:        O. Kechagias-Stamatis
COMMENTS:       21 pages, 14 figures
HIGHLIGHT:      Based on the current methodology trends, we propose a taxonomy for the SAR ATR architectures, along with a direct comparison of the strengths and weaknesses of each method under both standard and extended operational conditions.

151, TITLE:     Fast Adaptation via Policy-Dynamics Value Functions
http://arxiv.org/abs/2007.02879
AUTHORS:        Roberta Raileanu ; Max Goldstein ; Arthur Szlam ; Rob Fergus
HIGHLIGHT:      We introduce Policy-Dynamics Value Functions (PD-VF), a novel approach for rapidly adapting to dynamics different from those previously seen in training.

152, TITLE:     Fuzzy Integral = Contextual Linear Order Statistic
http://arxiv.org/abs/2007.02874
AUTHORS:        Derek Anderson ; Matthew Deardorff ; Timothy Havens ; Siva Kakula ; Timothy Wilkin ; Muhammad Islam ; Anthony Pinar ; Andrew Buck
COMMENTS:       11 pages
HIGHLIGHT:      Benefits of our approach include scalability, improved integral/measure acquisition, generalizability, and explainable/interpretable models.

153, TITLE:     DART: Open-Domain Structured Data Record to Text Generation
http://arxiv.org/abs/2007.02871
AUTHORS:        Dragomir Radev ; Rui Zhang ; Amrit Rau ; Abhinand Sivaprasad ; Chiachun Hsieh ; Nazneen Fatema Rajani ; Xiangru Tang ; Aadit Vyas ; Neha Verma ; Pranav Krishna ; Yangxiaokang Liu ; Nadia Irwanto ; Jessica Pan ; Faiaz Rahman ; Ahmad Zaidi ; Murori Mutuma ; Yasin Tarabar ; Ankit Gupta ; Tao Yu ; Yi Chern Tan ; Xi Victoria Lin ; Caiming Xiong ; Richard Socher
HIGHLIGHT:      We introduce DART, a large dataset for open-domain structured data record to text generation.

154, TITLE:     Geometric Attention for Prediction of Differential Properties in 3D Point Clouds
http://arxiv.org/abs/2007.02571
AUTHORS:        Albert Matveev ; Alexey Artemov ; Evgeny Burnaev
HIGHLIGHT:      In this study, we present a geometric attention mechanism that can provide such properties in a learnable fashion.

155, TITLE:     Enhancing SAT solvers with glue variable predictions
http://arxiv.org/abs/2007.02559
AUTHORS:        Jesse Michael Han
COMMENTS:       8 pages, 5 figures
HIGHLIGHT:      We address all these limitations, using a simpler network architecture allowing CPU inference for even large industrial problems with millions of clauses, and training instead to predict {\em glue variables}---a target for which it is easier to generate labelled data, and which can also be formulated as a reinforcement learning task.

156, TITLE:     Fairness in machine learning: against false positive rate equality as a measure of fairness
http://arxiv.org/abs/2007.02890
AUTHORS:        Robert Long
HIGHLIGHT:      In this paper, I give an ethical framework for thinking about these measures and argue that, contrary to initial appearances, false positive rate equality does not track anything about fairness, and thus sets an incoherent standard for evaluating the fairness of algorithms.

157, TITLE:     LMVE at SemEval-2020 Task 4: Commonsense Validation and Explanation using Pretraining Language Model
http://arxiv.org/abs/2007.02540
AUTHORS:        Shilei Liu ; Yu Guo ; Bochao Li ; Feiliang Ren
COMMENTS:       Accepted in SemEval2020. 7 pages, 4 figures
HIGHLIGHT:      This paper describes our submission to subtask a and b of SemEval-2020 Task 4.

158, TITLE:     ModeNet: Mode Selection Network For Learned Video Coding
http://arxiv.org/abs/2007.02532
AUTHORS:        Théo Ladune ; Pierrick Philippe ; Wassim Hamidouche ; Lu Zhang ; Olivier Déforges
HIGHLIGHT:      In this paper, a mode selection network (ModeNet) is proposed to enhance deep learning-based video compression.

159, TITLE:     Computational Complexity Characterization of Protecting Elections from Bribery
http://arxiv.org/abs/2007.02533
AUTHORS:        Lin Chen ; Ahmed Sunny ; Lei Xu ; Shouhuai Xu ; Zhimin Gao ; Yang Lu ; Weidong Shi ; Nolan Shah
COMMENTS:       28 Pages. The Article has been accepted in the 26th International Computing and Combinatorics Conference (COCOON 2020)
HIGHLIGHT:      The goal of this paper is to give a full picture on the complexity of protection problems.

160, TITLE:     Augment Yourself: Mixed Reality Self-Augmentation Using Optical See-through Head-mounted Displays and Physical Mirrors
http://arxiv.org/abs/2007.02884
AUTHORS:        Mathias Unberath ; Kevin Yu ; Roghayeh Barmaki ; Alex Johnson ; Nassir Navab
COMMENTS:       This manuscript was initially submitted to IEEE VR TVCG 2018 on November 22, 2017
HIGHLIGHT:      In this paper, we propose a novel concept and prototype system that combines OST HMDs and physical mirrors to enable self-augmentation and provide an immersive MR environment centered around the user.

161, TITLE:     Diagnosis of Coronary Artery Disease Using Artificial Intelligence Based Decision Support System
http://arxiv.org/abs/2007.02854
AUTHORS:        Noor Akhmad Setiawan ; Paruvachi Ammasai Venkatachalam ; Ahmad Fadzil M Hani
HIGHLIGHT:      The knowledge base of fuzzy decision support system is taken by using rules extraction method based on Rough Set Theory.

162, TITLE:     Counterfactual Data Augmentation using Locally Factored Dynamics
http://arxiv.org/abs/2007.02863
AUTHORS:        Silviu Pitis ; Elliot Creager ; Animesh Garg
COMMENTS:       12 pages (+12 appendix). Code available at \url{https://github.com/spitis/mrl}
HIGHLIGHT:      We propose an approach to inferring these structures given an object-oriented state representation, as well as a novel algorithm for model-free Counterfactual Data Augmentation (CoDA).

163, TITLE:     Model-based Exploration of the Frontier of Behaviours for Deep Learning System Testing
http://arxiv.org/abs/2007.02787
AUTHORS:        Vincenzo Riccio ; Paolo Tonella
COMMENTS:       To be published in the Proceedings of the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2020); 13 pages, 6 figures
HIGHLIGHT:      In this paper, we introduce the notion of frontier of behaviours, i.e., the inputs at which the DL system starts to misbehave.

164, TITLE:     Exploration of Optimized Semantic Segmentation Architectures for edge-Deployment on Drones
http://arxiv.org/abs/2007.02839
AUTHORS:        Vivek Parmar ; Narayani Bhatia ; Shubham Negi ; Manan Suri
HIGHLIGHT:      In this paper, we present an analysis on the impact of network parameters for semantic segmentation architectures in context of UAV data processing.

165, TITLE:     Intelligent Reflecting Surface Aided Wireless Communications: A Tutorial
http://arxiv.org/abs/2007.02759
AUTHORS:        Qingqing Wu ; Shuowen Zhang ; Beixiong Zheng ; Changsheng You ; Rui Zhang
COMMENTS:       IEEE TCOM EIC Invited Paper.A tutorial paper on IRS
HIGHLIGHT:      In this paper, we provide a tutorial overview of IRS-aided wireless communication to address the above issues, and elaborate its reflection and channel models, hardware architecture and practical constraints, as well as various appealing applications in wireless networks.

166, TITLE:     Sentiment Polarity Detection on Bengali Book Reviews Using Multinomial Naive Bayes
http://arxiv.org/abs/2007.02758
AUTHORS:        Eftekhar Hossain ; Omar Sharif ; Mohammed Moshiul Hoque
COMMENTS:       12 pages, ICACIE 2020, Will be published by Advances in Intelligent Systems and Computing (AISC) series of Springer
HIGHLIGHT:      This work introduces a machine learning-based technique to determine sentiment polarities (either positive or negative category) from Bengali book reviews.

167, TITLE:     robo-gym -- An Open Source Toolkit for Distributed Deep Reinforcement Learning on Real and Simulated Robots
http://arxiv.org/abs/2007.02753
AUTHORS:        Matteo Lucchi ; Friedemann Zindler ; Stephan Mühlbacher-Karrer ; Horst Pichler
HIGHLIGHT:      In order to increase the use of DRL with real robots and reduce the gap between simulation and real world robotics, we propose an open source toolkit: robo-gym.

168, TITLE:     Building Reservoir Computing Hardware Using Low Energy-Barrier Magnetics
http://arxiv.org/abs/2007.02766
AUTHORS:        Samiran Ganguly ; Avik W. Ghosh
COMMENTS:       To be presented at International Conference on Neuromorphic Systems 2020
HIGHLIGHT:      In this work we discuss using in-depth simulation studies a way to construct hardware reservoir computers using an analog stochastic neuron cell built from a low energy-barrier magnet based magnetic tunnel junction and a few transistors.

169, TITLE:     Egocentric Action Recognition by Video Attention and Temporal Context
http://arxiv.org/abs/2007.01883
AUTHORS:        Juan-Manuel Perez-Rua ; Antoine Toisoul ; Brais Martinez ; Victor Escorcia ; Li Zhang ; Xiatian Zhu ; Tao Xiang
COMMENTS:       EPIC-Kitchens challenges@CVPR 2020
HIGHLIGHT:      We present the submission of Samsung AI Centre Cambridge to the CVPR2020 EPIC-Kitchens Action Recognition Challenge.

170, TITLE:     Refined Analysis of the Asymptotic Complexity of the Number Field Sieve
http://arxiv.org/abs/2007.02730
AUTHORS:        Aude Le Gluher ; Pierre-Jean Spaenlehauer ; Emmanuel Thomé
HIGHLIGHT:      The aim of this paper is to find optimal asymptotic choices of the parameters of NFS as $N$ grows, in order to minimize its heuristic asymptotic computational cost.

171, TITLE:     Black-box Adversarial Example Generation with Normalizing Flows
http://arxiv.org/abs/2007.02734
AUTHORS:        Hadi M. Dolatabadi ; Sarah Erfani ; Christopher Leckie
COMMENTS:       Accepted to the 2nd workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models (ICML 2020), Virtual Conference
HIGHLIGHT:      In this paper, we propose a novel black-box adversarial attack using normalizing flows.

172, TITLE:     Multi-Objective Neural Architecture Search Based on Diverse Structures and Adaptive Recommendation
http://arxiv.org/abs/2007.02749
AUTHORS:        Chunnan Wang ; Hongzhi Wang ; Guocheng Feng ; Fei Geng
COMMENTS:       11pages
HIGHLIGHT:      Motivated by this, we propose MoARR algorithm, which utilizes the existing research results and historical information to quickly find architectures that are both lightweight and accurate.

173, TITLE:     Towards Game-Playing AI Benchmarks via Performance Reporting Standards
http://arxiv.org/abs/2007.02742
AUTHORS:        Vanessa Volz ; Boris Naujoks
COMMENTS:       IEEE Conference on Games 2020
HIGHLIGHT:      In this paper, we propose reporting guidelines for AI game-playing performance that, if followed, provide information suitable for unbiased comparisons between different AI approaches.

174, TITLE:     KRW Composition Theorems via Lifting
http://arxiv.org/abs/2007.02740
AUTHORS:        Susanna F. de Rezende ; Or Meir ; Jakob Nordström ; Toniann Pitassi ; Robert Robere
HIGHLIGHT:      In this work, we extend significantly the range of inner functions that can be handled.

175, TITLE:     A Few-Shot Sequential Approach for Object Counting
http://arxiv.org/abs/2007.01899
AUTHORS:        Negin Sokhandan ; Pegah Kamousi ; Alejandro Posada ; Eniola Alese ; Negar Rostamzadeh
HIGHLIGHT:      In this work, we address the problem of few-shot multi-classobject counting with point-level annotations.

176, TITLE:     Dynamic memory to alleviate catastrophic forgetting in continuous learning settings
http://arxiv.org/abs/2007.02639
AUTHORS:        Johannes Hofmanninger ; Matthias Perkonigg ; James A. Brink ; Oleg Pianykh ; Christian Herold ; Georg Langs
COMMENTS:       Accepted at MICCAI 2020
HIGHLIGHT:      Here, we address the problem of data shifts in a continuous learning scenario by adapting a model to unseen variations in the source domain while counteracting catastrophic forgetting effects.

177, TITLE:     Joint learning of Social Groups, Individuals Action and Sub-group Activities in Videos
http://arxiv.org/abs/2007.02632
AUTHORS:        Mahsa Ehsanpour ; Alireza Abedin ; Fatemeh Saleh ; Javen Shi ; Ian Reid ; Hamid Rezatofighi
COMMENTS:       Accepted in the European Conference On Computer Vision (ECCV) 2020
HIGHLIGHT:      In this paper, we solve the problem of simultaneously grouping people by their social interactions, predicting their individual actions and the social activity of each social group, which we call the social task.

178, TITLE:     TLIO: Tight Learned Inertial Odometry
http://arxiv.org/abs/2007.01867
AUTHORS:        Wenxin Liu ; David Caruso ; Eddy Ilg ; Jing Dong ; Anastasios I. Mourikis ; Kostas Daniilidis ; Vijay Kumar ; Jakob Engel
HIGHLIGHT:      In this work we propose a tightly-coupled Extended Kalman Filter framework for IMU-only state estimation.

179, TITLE:     CareCall: a Call-Based Active Monitoring Dialog Agent for Managing COVID-19 Pandemic
http://arxiv.org/abs/2007.02642
AUTHORS:        Sang-Woo Lee ; Hyunhoon Jung ; SukHyun Ko ; Sunyoung Kim ; Hyewon Kim ; Kyoungtae Doh ; Hyunjung Park ; Joseph Yeo ; Sang-Houn Ok ; Joonhaeng Lee ; Seongjae Choi ; SeungTae Hwang ; Eun-Young Park ; Gwang-Ja Ma ; Seok-Joo Han ; Kwang-Seung Cha ; Nako Sung ; Jung-Woo Ha
HIGHLIGHT:      To address this issue, we introduce CareCall, a call-based dialog agent which is deployed for active monitoring in Korea and Japan.

180, TITLE:     Community detection and Social Network analysis based on the Italian wars of the 15th century
http://arxiv.org/abs/2007.02641
AUTHORS:        J. Fumanal-Idocin ; A. Alonso-Betanzos ; O. Cordón ; H. Bustince ; M. Minárová
COMMENTS:       Accepted in Future Generation Computer Systems
HIGHLIGHT:      In the present article we study social network modelling using human interaction as a basis. To do so, we propose a new set of functions, Affinities, designed to capture the nature of the local interactions among each pair of actors in a network.

181, TITLE:     Understanding and Improving Fast Adversarial Training
http://arxiv.org/abs/2007.02617
AUTHORS:        Maksym Andriushchenko ; Nicolas Flammarion
HIGHLIGHT:      Based on this observation, we propose a new regularization method, GradAlign, that prevents catastrophic overfitting by explicitly maximizing the gradient alignment inside the perturbation set and improves the quality of the FGSM solution.

182, TITLE:     Learning Spoken Language Representations with Neural Lattice Language Modeling
http://arxiv.org/abs/2007.02629
AUTHORS:        Chao-Wei Huang ; Yun-Nung Chen
COMMENTS:       Published in ACL 2020 as a short paper
HIGHLIGHT:      We propose a framework that trains neural lattice language models to provide contextualized representations for spoken language understanding tasks.

183, TITLE:     Smart Home, security concerns of IoT
http://arxiv.org/abs/2007.02628
AUTHORS:        Alessandro Ecclesie Agazzi
HIGHLIGHT:      Default and weak passwords, cheap materials and hardware, and unencrypted communication are identified as the principal threats and vulnerabilities of IoT devices.

184, TITLE:     NAPPO: Modular and scalable reinforcement learning in pytorch
http://arxiv.org/abs/2007.02622
AUTHORS:        Albert Bou ; Gianni De Fabritiis
COMMENTS:       8 pages, 5 figures
HIGHLIGHT:      Here, we present NAPPO, a pytorch-based library for RL which provides scalable proximal policy optimization (PPO) implementations in a simple, modular package.

185, TITLE:     A Convolutional Approach to Vertebrae Detection and Labelling in Whole Spine MRI
http://arxiv.org/abs/2007.02606
AUTHORS:        Rhydian Windsor ; Amir Jamaludin ; Timor Kadir ; Rhydian Windsor
COMMENTS:       Accepted full paper to Medical Image Computing and Computer Assisted Intervention 2020. 11 pages plus appendix
HIGHLIGHT:      We propose a novel convolutional method for the detection and identification of vertebrae in whole spine MRIs.

186, TITLE:     Relevance Transformer: Generating Concise Code Snippets with Relevance Feedback
http://arxiv.org/abs/2007.02609
AUTHORS:        Carlos Gemmell ; Federico Rossetto ; Jeffrey Dalton
HIGHLIGHT:      In this work we introduce and study modern Transformer architectures for this task.

187, TITLE:     In the Wild: From ML Models to Pragmatic ML Systems
http://arxiv.org/abs/2007.02519
AUTHORS:        Matthew Wallingford ; Aditya Kusupati ; Keivan Alizadeh-Vahid ; Aaron Walsman ; Aniruddha Kembhavi ; Ali Farhadi
COMMENTS:       16 pages, 6 figures, Code is available at https://github.com/RAIVNLab/InTheWild
HIGHLIGHT:      To foster the research required to extend ML models to ML systems, we introduce a unified learning & evaluation framework - iN thE wilD (NED).

188, TITLE:     Traffic Agent Trajectory Prediction Using Social Convolution and Attention Mechanism
http://arxiv.org/abs/2007.02515
AUTHORS:        Tao Yang ; Zhixiong Nan ; He Zhang ; Shitao Chen ; Nanning Zheng
HIGHLIGHT:      In this paper, we propose a model to predict the trajectories of target agents around an autonomous vehicle.

189, TITLE:     EDSL: An Encoder-Decoder Architecture with Symbol-Level Features for Printed Mathematical Expression Recognition
http://arxiv.org/abs/2007.02517
AUTHORS:        Yingnan Fu ; Tingting Liu ; Ming Gao ; Aoying Zhou
HIGHLIGHT:      In this paper, we propose a new method named EDSL, shorted for encoder-decoder with symbol-level features, to identify the printed mathematical expressions from images.

190, TITLE:     Jump Operator Planning: Goal-Conditioned Policy Ensembles and Zero-Shot Transfer
http://arxiv.org/abs/2007.02527
AUTHORS:        Thomas J. Ringstrom ; Mohammadhosein Hasanbeig ; Alessandro Abate
HIGHLIGHT:      In Hierarchical Control, compositionality, abstraction, and task-transfer are crucial for designing versatile algorithms which can solve a variety of problems with maximal representational reuse.

191, TITLE:     Deep Learning for Anomaly Detection: A Review
http://arxiv.org/abs/2007.02500
AUTHORS:        Guansong Pang ; Chunhua Shen ; Longbing Cao ; Anton van den Hengel
COMMENTS:       Survey paper, 36 pages, 180 references, 2 figures, 3 tables
HIGHLIGHT:      We review their key intuitions, objective functions, underlying assumptions, advantages and disadvantages, and discuss how they address the aforementioned challenges.

192, TITLE:     On the weight and density bounds of polynomial threshold functions
http://arxiv.org/abs/2007.02509
AUTHORS:        Erhan Oztop ; Minoru Asada
COMMENTS:       To be submitted to SIAM Journal on Discrete Mathematics or Discrete Mathematics. Main author: Erhan Oztop
HIGHLIGHT:      In this report, we show that all n-variable Boolean function can be represented as polynomial threshold functions (PTF) with at most $0.75 \times 2^n$ non-zero integer coefficients and give an upper bound on the absolute value of these coefficients.

193, TITLE:     Tree-Augmented Cross-Modal Encoding for Complex-Query Video Retrieval
http://arxiv.org/abs/2007.02503
AUTHORS:        Xun Yang ; Jianfeng Dong ; Yixin Cao ; Xun Wang ; Meng Wang ; Tat-Seng Chua
COMMENTS:       Accepted For 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020)
HIGHLIGHT:      To facilitate video retrieval with complex queries, we propose a Tree-augmented Cross-modal Encoding method by jointly learning the linguistic structure of queries and the temporal representation of videos.



==========Updates to Previous Papers==========
1, TITLE:       Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey
http://arxiv.org/abs/2007.00753
AUTHORS:        Samuel Henrique Silva ; Peyman Najafirad
COMMENTS:       20 pages, 9 figures, submited to IEEE Transactions on Knowledge and Data Engineering
HIGHLIGHT:      We provide a taxonomy to classify adversarial attacks and defenses, formulate the Robust Optimization problem in a min-max setting and divide it into 3 subcategories, namely: Adversarial (re)Training, Regularization Approach, and Certified Defenses.

2, TITLE:       A New Channel Boosted Convolutional Neural Network using Transfer Learning
http://arxiv.org/abs/1804.08528
AUTHORS:        Asifullah Khan ; Anabia Sohail ; Amna Ali
COMMENTS:       24 Pages, 5 Figures, 1 Table
HIGHLIGHT:      We present a novel architectural enhancement of Channel Boosting in a deep convolutional neural network (CNN).

3, TITLE:       Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies
http://arxiv.org/abs/2006.06091
AUTHORS:        Yu Huang ; Yue Chen
HIGHLIGHT:      Due to the limited space, we focus the analysis on several key areas, i.e. 2D and 3D object detection in perception, depth estimation from cameras, multiple sensor fusion on the data, feature and task level respectively, behavior modelling and prediction of vehicle driving and pedestrian trajectories.

4, TITLE:       Deep 3D Capture: Geometry and Reflectance from Sparse Multi-View Images
http://arxiv.org/abs/2003.12642
AUTHORS:        Sai Bi ; Zexiang Xu ; Kalyan Sunkavalli ; David Kriegman ; Ravi Ramamoorthi
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      We introduce a novel learning-based method to reconstruct the high-quality geometry and complex, spatially-varying BRDF of an arbitrary object from a sparse set of only six images captured by wide-baseline cameras under collocated point lighting.

5, TITLE:       Whence the Expected Free Energy?
http://arxiv.org/abs/2004.08128
AUTHORS:        Beren Millidge ; Alexander Tschantz ; Christopher L Buckley
COMMENTS:       24 pages, 0 figures. Reuploaded to correct typos in the original. Update 05-07-20 -- minor corrections
HIGHLIGHT:      In this paper, we investigate the origins of the EFE in detail and show that it is not simply "the free energy in the future".

6, TITLE:       Reinforcement Learning as Iterative and Amortised Inference
http://arxiv.org/abs/2006.10524
AUTHORS:        Beren Millidge ; Alexander Tschantz ; Anil K Seth ; Christopher L Buckley
COMMENTS:       initial upload; 05-07-20 -- updated with minor corrections
HIGHLIGHT:      In this paper, we utilise the control as inference framework to outline a novel classification scheme based on amortised and iterative inference.

7, TITLE:       Robust Single Rotation Averaging
http://arxiv.org/abs/2004.00732
AUTHORS:        Seong Hun Lee ; Javier Civera
COMMENTS:       Minor revisions
HIGHLIGHT:      We propose a novel method for single rotation averaging using the Weiszfeld algorithm.

8, TITLE:       TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition
http://arxiv.org/abs/2004.07493
AUTHORS:        Bill Yuchen Lin ; Dong-Ho Lee ; Ming Shen ; Ryan Moreno ; Xiao Huang ; Prashant Shiralkar ; Xiang Ren
HIGHLIGHT:      In this paper, we introduce "entity triggers," an effective proxy of human explanations for facilitating label-efficient learning of NER models.

9, TITLE:       4D MRI: Robust sorting of free breathing MRI slices for use in interventional settings
http://arxiv.org/abs/1910.01902
AUTHORS:        Gino Gulamhussene ; Fabian Joeres ; Marko Rak ; Maciej Pech ; Christian Hansen
COMMENTS:       16 pages, 11 figures
HIGHLIGHT:      Method: We propose a 4D MRI reconstruction method to capture an arbitrary number of breathing states.

10, TITLE:      An Auto-Context Deformable Registration Network for Infant Brain MRI
http://arxiv.org/abs/2005.09230
AUTHORS:        Dongming Wei ; Sahar Ahmad ; Yunzhi Huang ; Lei Ma ; Zhengwang Wu ; Gang Li ; Li Wang ; Qian Wang ; Pew-Thian Yap ; Dinggang Shen
HIGHLIGHT:      In this paper, we propose an infant-dedicated deep registration network that uses the auto-context strategy to gradually refine the deformation fields to obtain highly accurate correspondences.

11, TITLE:      RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers
http://arxiv.org/abs/1911.04942
AUTHORS:        Bailin Wang ; Richard Shin ; Xiaodong Liu ; Oleksandr Polozov ; Matthew Richardson
COMMENTS:       Fix some errors of ACL 2020 camera-ready; 12 pages, 5 figures, 7 tables. arXiv admin note: text overlap with arXiv:1906.11790
HIGHLIGHT:      We present a unified framework, based on the relation-aware self-attention mechanism, to address schema encoding, schema linking, and feature representation within a text-to-SQL encoder.

12, TITLE:      Gradient Temporal-Difference Learning with Regularized Corrections
http://arxiv.org/abs/2007.00611
AUTHORS:        Sina Ghiassian ; Andrew Patterson ; Shivam Garg ; Dhawal Gupta ; Adam White ; Martha White
COMMENTS:       22 pages. Accepted to ICML 2020
HIGHLIGHT:      In this paper, we introduce a new method called TD with Regularized Corrections (TDRC), that attempts to balance ease of use, soundness, and performance.

13, TITLE:      Bias-based Universal Adversarial Patch Attack for Automatic Check-out
http://arxiv.org/abs/2005.09257
AUTHORS:        Aishan Liu ; Jiakai Wang ; Xianglong Liu ; Bowen Cao ; Chongzhi Zhang ; Hang Yu
HIGHLIGHT:      To address the problem, this paper proposes a bias-based framework to generate class-agnostic universal adversarial patches with strong generalization ability, which exploits both the perceptual and semantic bias of models.

14, TITLE:      Constrained Linear Data-feature Mapping for Image Classification
http://arxiv.org/abs/1911.10428
AUTHORS:        Juncai He ; Yuyan Chen ; Lian Zhang ; Jinchao Xu
COMMENTS:       15 page, 2 figures
HIGHLIGHT:      In this paper, we propose a constrained linear data-feature mapping model as an interpretable mathematical model for image classification using convolutional neural network (CNN) such as the ResNet.

15, TITLE:      Deep Low-rank Prior in Dynamic MR Imaging
http://arxiv.org/abs/2006.12090
AUTHORS:        Ziwen Ke ; Wenqi Huang ; Jing Cheng ; Zhuoxu Cui ; Sen Jia ; Haifeng Wang ; Xin Liu ; Hairong Zheng ; Leslie Ying ; Yanjie Zhu ; Dong Liang
COMMENTS:       13 pages, 8 figures
HIGHLIGHT:      In this paper, a learned singular value thresholding (Learned-SVT) operation is proposed to explore deep low-rank prior in dynamic MR imaging to obtain improved reconstruction results.

16, TITLE:      VoxelPose: Towards Multi-Camera 3D Human Pose Estimation in Wild Environment
http://arxiv.org/abs/2004.06239
AUTHORS:        Hanyue Tu ; Chunyu Wang ; Wenjun Zeng
HIGHLIGHT:      We present an approach to estimate 3D poses of multiple people from multiple camera views.

17, TITLE:      Fairness by Learning Orthogonal Disentangled Representations
http://arxiv.org/abs/2003.05707
AUTHORS:        Mhd Hasan Sarhan ; Nassir Navab ; Abouzar Eslami ; Shadi Albarqouni
HIGHLIGHT:      In this paper, we propose a novel disentanglement approach to invariant representation problem.

18, TITLE:      Projective clone homomorphisms
http://arxiv.org/abs/1409.4601
AUTHORS:        Manuel Bodirsky ; Michael Pinsker ; András Pongrácz
COMMENTS:       13 pages
HIGHLIGHT:      We investigate the relationship between the existence of a clone homomorphism to the projection clone, and the existence of such a homomorphism which is continuous and thus meets the above criterion.

19, TITLE:      Worse WER, but Better BLEU? Leveraging Word Embedding as Intermediate in Multitask End-to-End Speech Translation
http://arxiv.org/abs/2005.10678
AUTHORS:        Shun-Po Chuang ; Tzu-Wei Sung ; Alexander H. Liu ; Hung-yi Lee
COMMENTS:       Accepted by ACL 2020
HIGHLIGHT:      Because whether the output of the recognition decoder has the correct semantics is more critical than its accuracy, we propose to improve the multitask ST model by utilizing word embedding as the intermediate.

20, TITLE:      An encoder-decoder-based method for COVID-19 lung infection segmentation
http://arxiv.org/abs/2007.00861
AUTHORS:        Omar Elharrouss ; Nandhini Subramanian ; Somaya Al-Maadeed
HIGHLIGHT:      This paper proposes a multi-task deep-learning-based method for lung infection segmentation using CT-scan images.

21, TITLE:      On tensor rank and commuting matrices
http://arxiv.org/abs/2006.02374
AUTHORS:        Pascal Koiran
COMMENTS:       New material in this version: * More extensive presentation of prior work, and in particular of rank methods and barrier results. * Discussion of embedding in commuting matrices versus commuting diagonalizable matrices
HIGHLIGHT:      In this paper we propose a generalization of the approach used by Strassen in the proof of his 3n/2 border rank lower bound.

22, TITLE:      Improved Large-margin Softmax Loss for Speaker Diarisation
http://arxiv.org/abs/1911.03970
AUTHORS:        Yassir Fathullah ; Chao Zhang ; Philip C. Woodland
COMMENTS:       ICASSP 2020
HIGHLIGHT:      Therefore, this paper introduces a general approach to the large-margin softmax loss without any approximations to improve the quality of speaker embeddings for diarisation.

23, TITLE:      Non-Volume Preserving-based Feature Fusion Approach to Group-Level Expression Recognition on Crowd Videos
http://arxiv.org/abs/1811.11849
AUTHORS:        Kha Gia Quach ; Ngan Le ; Khoa Luu ; Chi Nhan Duong ; Ibsa Jalata ; Kaushik Roy
COMMENTS:       Under review at Patter Recognition
HIGHLIGHT:      In this paper, we propose an effective deep feature level fusion mechanism to model the spatial-temporal information in the crowd videos.

24, TITLE:      MetaInv-Net: Meta Inversion Network for Sparse View CT Image Reconstruction
http://arxiv.org/abs/2006.00171
AUTHORS:        Haimiao Zhang ; Baodong Liu ; Hengyong Yu ; Bin Dong
COMMENTS:       11 pages
HIGHLIGHT:      In this paper, we propose a new deep learning based model for CT image reconstruction with the backbone network architecture built by unrolling an iterative algorithm.

25, TITLE:      Spatiotemporal Attacks for Embodied Agents
http://arxiv.org/abs/2005.09161
AUTHORS:        Aishan Liu ; Tairan Huang ; Xianglong Liu ; Yitao Xu ; Yuqing Ma ; Xinyun Chen ; Stephen J. Maybank ; Dacheng Tao
COMMENTS:       Accepted on ECCV 2020
HIGHLIGHT:      In this work, we take the first step to study adversarial attacks for embodied agents.

26, TITLE:      Compressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations
http://arxiv.org/abs/2006.15271
AUTHORS:        Dongdong Chen ; Mike E. Davies ; Mohammad Golbabaee
COMMENTS:       To appear in MICCAI 2020
HIGHLIGHT:      To address this, we propose ProxNet, a learned proximal gradient descent framework that directly incorporates the forward acquisition and Bloch dynamic models within a recurrent learning mechanism.

27, TITLE:      Crossing Variational Autoencoders for Answer Retrieval
http://arxiv.org/abs/2005.02557
AUTHORS:        Wenhao Yu ; Lingfei Wu ; Qingkai Zeng ; Shu Tao ; Yu Deng ; Meng Jiang
COMMENTS:       Accepted to ACL 2020
HIGHLIGHT:      In this work, we propose to cross variational auto-encoders by generating questions with aligned answers and generating answers with aligned questions.

28, TITLE:      Lesion Mask-based Simultaneous Synthesis of Anatomic and MolecularMR Images using a GAN
http://arxiv.org/abs/2006.14761
AUTHORS:        Pengfei Guo ; Puyang Wang ; Jinyuan Zhou ; Vishal Patel ; Shanshan Jiang
COMMENTS:       MICCAI 2020
HIGHLIGHT:      In this paper, we propose a generative adversarial network (GAN), which can simultaneously synthesize data from arbitrary manipulated lesion information on multiple anatomic and molecular MRI sequences, including T1-weighted (T1w), gadolinium enhanced T1w (Gd-T1w), T2-weighted (T2w), fluid-attenuated inversion recovery (FLAIR), and amide proton transfer-weighted (APTw).

29, TITLE:      Removing Algebraic Data Types from Constrained Horn Clauses Using Difference Predicates
http://arxiv.org/abs/2004.07749
AUTHORS:        Emanuele De Angelis ; Fabio Fioravanti ; Alberto Pettorossi ; Maurizio Proietti
COMMENTS:       10th International Joint Conference on Automated Reasoning (IJCAR 2020) - version with appendix; added DOI of the final authenticated Springer publication; minor corrections
HIGHLIGHT:      We propose a new technique for transforming CHCs with ADTs into CHCs where predicates are defined over basic types, such as integers and booleans, only.

30, TITLE:      Linear Mode Connectivity and the Lottery Ticket Hypothesis
http://arxiv.org/abs/1912.05671
AUTHORS:        Jonathan Frankle ; Gintare Karolina Dziugaite ; Daniel M. Roy ; Michael Carbin
COMMENTS:       Published in ICML 2020. This submission subsumes arXiv:1903.01611 ("Stabilizing the Lottery Ticket Hypothesis" and "The Lottery Ticket Hypothesis at Scale")
HIGHLIGHT:      We use this technique to study iterative magnitude pruning (IMP), the procedure used by work on the lottery ticket hypothesis to identify subnetworks that could have trained in isolation to full accuracy.

31, TITLE:      Invariant Transform Experience Replay: Data Augmentation for Deep Reinforcement Learning
http://arxiv.org/abs/1909.10707
AUTHORS:        Yijiong Lin ; Jiancong Huang ; Matthieu Zimmer ; Yisheng Guan ; Juan Rojas ; Paul Weng
COMMENTS:       8 pages, 11 figures, additional 3 pages for appendix. IEEE Robotics and Automation Letters (RAL), 2020. Also in: Intelligent Robots and Systems (IROS)
HIGHLIGHT:      Based on this data augmentation idea, we formulate a general framework, called Invariant Transform Experience Replay that we present with two techniques: (i) Kaleidoscope Experience Replay exploits reflectional symmetries and (ii) Goal-augmented Experience Replay which takes advantage of lax goal definitions.

32, TITLE:      An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices
http://arxiv.org/abs/2001.07710
AUTHORS:        Xiaolong Ma ; Wei Niu ; Tianyun Zhang ; Sijia Liu ; Sheng Lin ; Hongjia Li ; Xiang Chen ; Jian Tang ; Kaisheng Ma ; Bin Ren ; Yanzhi Wang
COMMENTS:       Paper accepted in the 16th European Conference on Computer Vision (ECCV 2020)
HIGHLIGHT:      To solve the problem, we introduce a new sparsity dimension, namely pattern-based sparsity that comprises pattern and connectivity sparsity, and becoming both highly accurate and hardware friendly.

33, TITLE:      Comprehensive decision-strategy space exploration for efficient territorial planning strategies
http://arxiv.org/abs/1911.11460
AUTHORS:        Olivier Billaud ; Maxence Soubeyrand ; Sandra Luque ; Maxime Lenormand
COMMENTS:       12 pages, 7 figures + Appendix
HIGHLIGHT:      This paper presents a spatial sensitivity analysis to assess the impact of order weights determination in GIS-based Multi-Criteria Analysis by Ordered Weighted Averaging.

34, TITLE:      Domain Confusion with Self Ensembling for Unsupervised Adaptation
http://arxiv.org/abs/1810.04472
AUTHORS:        Jiawei Wang ; Zhaoshui He ; Chengjian Feng ; Zhouping Zhu ; Qinzhuang Lin ; Jun Lv ; Shengli Xie
COMMENTS:       The expression is ambiguous, which is not convenient for readers to understand, and in today's view, the conclusion of the paper is of little significance, so it is no longer open
HIGHLIGHT:      The experiments verified that our proposed approach can offer better performance than empirical art in a variety of unsupervised domain adaptation benchmarks.

35, TITLE:      Federated Visual Classification with Real-World Data Distribution
http://arxiv.org/abs/2003.08082
AUTHORS:        Tzu-Ming Harry Hsu ; Hang Qi ; Matthew Brown
HIGHLIGHT:      In this work, we characterize the effect these real-world data distributions have on distributed learning, using as a benchmark the standard Federated Averaging (FedAvg) algorithm. To do so, we introduce two new large-scale datasets for species and landmark classification, with realistic per-user data splits that simulate real-world edge learning scenarios.

36, TITLE:      PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning
http://arxiv.org/abs/2006.16779
AUTHORS:        Siqi Bao ; Huang He ; Fan Wang ; Hua Wu ; Haifeng Wang ; Wenquan Wu ; Zhen Guo ; Zhibin Liu ; Xinchao Xu
COMMENTS:       First four authors contributed equally to this work
HIGHLIGHT:      To build a high-quality open-domain chatbot, we introduce the effective training process of PLATO-2 via curriculum learning.

37, TITLE:      RigNet: Neural Rigging for Articulated Characters
http://arxiv.org/abs/2005.00559
AUTHORS:        Zhan Xu ; Yang Zhou ; Evangelos Kalogerakis ; Chris Landreth ; Karan Singh
COMMENTS:       SIGGRAPH 2020. Project page https://zhan-xu.github.io/rig-net/
HIGHLIGHT:      We present RigNet, an end-to-end automated method for producing animation rigs from input character models.

38, TITLE:      Technology Readiness Levels for Machine Learning Systems
http://arxiv.org/abs/2006.12497
AUTHORS:        Alexander Lavin ; Gregory Renard
HIGHLIGHT:      Drawing on experience in both spacecraft engineering and AI/ML (from research through product), we propose a proven systems engineering approach for machine learning development and deployment.

39, TITLE:      Designing for Human Rights in AI
http://arxiv.org/abs/2005.04949
AUTHORS:        Evgeni Aizenberg ; Jeroen van den Hoven
COMMENTS:       30 pages, 2 figures, pre-print of the paper accepted for publication in the journal Big Data & Society
HIGHLIGHT:      In this paper, we bridge this divide through the framework of Design for Values, drawing on methodologies of Value Sensitive Design and Participatory Design to present a roadmap for proactively engaging societal stakeholders to translate fundamental human rights into context-dependent design requirements through a structured, inclusive, and transparent process.

40, TITLE:      Adaptive Loss-aware Quantization for Multi-bit Networks
http://arxiv.org/abs/1912.08883
AUTHORS:        Zhongnan Qu ; Zimu Zhou ; Yun Cheng ; Lothar Thiele
COMMENTS:       To appear in CVPR 2020; Code available at https://github.com/zqu1992/ALQ
HIGHLIGHT:      We propose Adaptive Loss-aware Quantization (ALQ), a new MBN quantization pipeline that is able to achieve an average bitwidth below one-bit without notable loss in inference accuracy.

41, TITLE:      On the Complexity of Modulo-q Arguments and the Chevalley-Warning Theorem
http://arxiv.org/abs/1912.04467
AUTHORS:        Mika Göös ; Pritish Kamath ; Katerina Sotiraki ; Manolis Zampetakis
COMMENTS:       To appear at the Computational Complexity Conference (CCC) 2020
HIGHLIGHT:      We study the search problem class $\mathrm{PPA}_q$ defined as a modulo-$q$ analog of the well-known $\textit{polynomial parity argument}$ class $\mathrm{PPA}$ introduced by Papadimitriou '94.

42, TITLE:      Biomechanics-informed Neural Networks for Myocardial Motion Tracking in MRI
http://arxiv.org/abs/2006.04725
AUTHORS:        Chen Qin ; Shuo Wang ; Chen Chen ; Huaqi Qiu ; Wenjia Bai ; Daniel Rueckert
COMMENTS:       The paper is early accepted by MICCAI 2020
HIGHLIGHT:      In contrast to most of the current approaches which impose explicit regularisation terms such as smoothness, in this paper we propose a novel method that can implicitly learn biomechanics-informed regularisation.

43, TITLE:      Derivative Manipulation for General Example Weighting
http://arxiv.org/abs/1905.11233
AUTHORS:        Xinshao Wang ; Elyor Kodirov ; Yang Hua ; Neil M. Robertson
COMMENTS:       Is it the right time to make a change from the design of loss function to the design of derivative directly?
HIGHLIGHT:      Therefore, we propose derivative manipulation (DM), a novel and general example weighting approach for training robust deep models under these adverse conditions.

44, TITLE:      GraphX$^{NET}-$ Chest X-Ray Classification Under Extreme Minimal Supervision
http://arxiv.org/abs/1907.10085
AUTHORS:        Angelica I. Aviles-Rivero ; Nicolas Papadakis ; Ruoteng Li ; Philip Sellars ; Qingnan Fan ; Robby T. Tan ; Carola-Bibiane Schönlieb
COMMENTS:       MICCAI 2019
HIGHLIGHT:      In this work, we introduce a novel semi-supervised framework for X-ray classification which is based on a graph-based optimisation model.

45, TITLE:      Inapproximability of the independent set polynomial in the complex plane
http://arxiv.org/abs/1711.00282
AUTHORS:        Ivona Bezakova ; Andreas Galanis ; Leslie Ann Goldberg ; Daniel Stefankovic
HIGHLIGHT:      We study the complexity of approximating the independent set polynomial $Z_G(\lambda)$ of a graph $G$ with maximum degree $\Delta$ when the activity $\lambda$ is a complex number.

46, TITLE:      Learning Semantically Enhanced Feature for Fine-Grained Image Classification
http://arxiv.org/abs/2006.13457
AUTHORS:        Wei Luo ; Hengmin Zhang ; Jun Li ; Xiu-Shen Wei
COMMENTS:       4 pages, 4 figures
HIGHLIGHT:      We aim to provide a computationally cheap yet effective approach for fine-grained image classification (FGIC) in this letter.

47, TITLE:      Extending Machine Language Models toward Human-Level Language Understanding
http://arxiv.org/abs/1912.05877
AUTHORS:        James L. McClelland ; Felix Hill ; Maja Rudolph ; Jason Baldridge ; Hinrich Schütze
HIGHLIGHT:      We describe the organization of the brain's distributed understanding system, which includes a fast learning system that addresses the memory problem.

48, TITLE:      Non-Autoregressive Coarse-to-Fine Video Captioning
http://arxiv.org/abs/1911.12018
AUTHORS:        Bang Yang ; Fenglin Liu ; Can Zhang ; Yuexian Zou
HIGHLIGHT:      In this paper, we propose a non-autoregressive decoding based model with a coarse-to-fine captioning procedure, i.e., Non-Autoregressive Coarse-to-Fine model, to alleviate these defects.

49, TITLE:      Graph modelling approaches for motorway traffic flow prediction
http://arxiv.org/abs/2006.14824
AUTHORS:        Adriana-Simona Mihaita ; Zac Papachatgis ; Marian-Andrei Rizoiu
HIGHLIGHT:      This paper presents two new spatial-temporal approaches for building accurate short-term prediction along a popular motorway in Sydney, by making use of the graph structure of the motorway network (including exits and entries).

50, TITLE:      TableBank: A Benchmark Dataset for Table Detection and Recognition
http://arxiv.org/abs/1903.01949
AUTHORS:        Minghao Li ; Lei Cui ; Shaohan Huang ; Furu Wei ; Ming Zhou ; Zhoujun Li
COMMENTS:       LREC 2020
HIGHLIGHT:      We present TableBank, a new image-based table detection and recognition dataset built with novel weak supervision from Word and Latex documents on the internet.

51, TITLE:      Neural Relation Prediction for Simple Question Answering over Knowledge Graph
http://arxiv.org/abs/2002.07715
AUTHORS:        Amin Abolghasemi ; Saeedeh Momtazi
HIGHLIGHT:      In this paper, we propose an instance-based method to capture the underlying relation of question and to this aim, we detect matching paraphrases of a new question which share the same relation, and their corresponding relation is selected as our prediction.

52, TITLE:      A Holistic Representation Guided Attention Network for Scene Text Recognition
http://arxiv.org/abs/1904.01375
AUTHORS:        Lu Yang ; Peng Wang ; Hui Li ; Zhen Li ; Yanning Zhang
COMMENTS:       Accept by Journal of NEUROCOMPUTING
HIGHLIGHT:      In this work, we propose a simple yet strong approach for scene text recognition.

53, TITLE:      Sequential Domain Adaptation through Elastic Weight Consolidation for Sentiment Analysis
http://arxiv.org/abs/2007.01189
AUTHORS:        Avinash Madasu ; Vijjini Anvesh Rao
COMMENTS:       Accepted at 25th International Conference on Pattern Recognition, January 2021, Milan, Italy
HIGHLIGHT:      We propose a model-independent framework - Sequential Domain Adaptation (SDA).

54, TITLE:      On the list recoverability of randomly punctured codes
http://arxiv.org/abs/2005.02478
AUTHORS:        Ben Lund ; Aditya Potukuchi
COMMENTS:       13 pages, several changes in introductory sections
HIGHLIGHT:      We show that a random puncturing of a code with good distance is list recoverable beyond the Johnson bound.

55, TITLE:      It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction
http://arxiv.org/abs/2004.02025
AUTHORS:        Karttikeya Mangalam ; Harshayu Girase ; Shreyas Agarwal ; Kuan-Hui Lee ; Ehsan Adeli ; Jitendra Malik ; Adrien Gaidon
COMMENTS:       14 pages, 6 figures, 3 tables
HIGHLIGHT:      In this work, we present Predicted Endpoint Conditioned Network (PECNet) for flexible human trajectory prediction.

56, TITLE:      A Generalized Kernel Risk Sensitive Loss for Robust Two-Dimensional Singular Value Decomposition
http://arxiv.org/abs/2005.04671
AUTHORS:        Miaohua Zhang ; Yongsheng Gao
COMMENTS:       Under Consideration by "Pattern Recognition"
HIGHLIGHT:      To overcome this problem, we propose a robust 2DSVD framework based on a generalized kernel risk sensitive loss (GKRSL-2DSVD) which is more robust to noise and and outliers.

57, TITLE:      From Checking to Inference: Actual Causality Computations as Optimization Problems
http://arxiv.org/abs/2006.03363
AUTHORS:        Amjad Ibrahim ; Alexander Pretschner
COMMENTS:       ATVA 2020 The 18th International Symposium on Automated Technology for Verification and Analysis
HIGHLIGHT:      In this paper, we present a novel approach to formulate different notions of causal reasoning, over binary acyclic models, as optimization problems, based on quantifiable notions within counterfactual computations.

58, TITLE:      DeepACEv2: Automated Chromosome Enumeration in Metaphase Cell Images Using Deep Convolutional Neural Networks
http://arxiv.org/abs/1910.11091
AUTHORS:        Chunlong Luo ; Li Xiao ; Tianqi Yu ; Yufan Luo ; Manqing Wang ; Fuhai Yu ; Yinhao Li ; Chan Tian ; Jie Qiao
COMMENTS:       There is some fatal errors and I need to made the change
HIGHLIGHT:      To automate the enumeration process, we develop a chromosome enumeration framework, DeepACEv2, based on the region based object detection scheme.

59, TITLE:      Exemplar based Generation and Data Augmentation using Exemplar VAEs
http://arxiv.org/abs/2004.04795
AUTHORS:        Sajad Norouzi ; David J. Fleet ; Mohammad Norouzi
HIGHLIGHT:      We propose Retrieval Augmented Training (RAT) that uses approximate nearest neighbor search in the latent space to speed up training based on a novel lower bound on log marginal likelihood.

60, TITLE:      Visual Identification of Individual Holstein-Friesian Cattle via Deep Metric Learning
http://arxiv.org/abs/2006.09205
AUTHORS:        William Andrew ; Jing Gao ; Siobhan Mullan ; Neill Campbell ; Andrew W Dowsey ; Tilo Burghardt
COMMENTS:       37 pages, 14 figures, 2 tables; Submitted to Computers and Electronics in Agriculture; Source code and network weights available at https://github.com/CWOA/MetricLearningIdentification; OpenCows2020 dataset available at https://doi.org/10.5523/bris.10m32xl88x2b61zlkkgz3fml17
HIGHLIGHT:      Existing approaches rely on markings, tags or wearables with a variety of maintenance requirements, whereas we present a totally hands-off method for the automated detection, localisation, and identification of individual animals from overhead imaging in an open herd setting, i.e. where new additions to the herd are identified without re-training.

61, TITLE:      MASTER: Multi-Aspect Non-local Network for Scene Text Recognition
http://arxiv.org/abs/1910.02562
AUTHORS:        Ning Lu ; Wenwen Yu ; Xianbiao Qi ; Yihao Chen ; Ping Gong ; Rong Xiao
COMMENTS:       Ning Lu and Wenwen Yu are co-first authors. Code at https://github.com/jiangxiluning/MASTER-TF
HIGHLIGHT:      MASTER: Multi-Aspect Non-local Network for Scene Text Recognition

62, TITLE:      Insignificant Choice Polynomial Time
http://arxiv.org/abs/2005.04598
AUTHORS:        Klaus-Dieter Schewe
COMMENTS:       51 pages
HIGHLIGHT:      In this article we first refute Gurevich's conjecture.

63, TITLE:      Multi-Head Attention based Probabilistic Vehicle Trajectory Prediction
http://arxiv.org/abs/2004.03842
AUTHORS:        Hayoung Kim ; Dongchan Kim ; Gihoon Kim ; Jeongmin Cho ; Kunsoo Huh
COMMENTS:       6 pages, 5 figures, 2020 IEEE Intelligent Vehicles Symposium (IV)
HIGHLIGHT:      This paper presents online-capable deep learning model for probabilistic vehicle trajectory prediction.

64, TITLE:      Existential Theory of the Reals Completeness of Stationary Nash Equilibria in Perfect Information Stochastic Games
http://arxiv.org/abs/2006.08314
AUTHORS:        Kristoffer Arnsfelt Hansen ; Steffan Christ Sølvsten
COMMENTS:       19 pages, 9 figures, to be published at the 45th International Symposium on Mathematical Foundations of Computer Science
HIGHLIGHT:      We show that the problem of deciding whether in a multi-player perfect information recursive game (i.e. a stochastic game with terminal rewards) there exists a stationary Nash equilibrium ensuring each player a certain payoff is Existential Theory of the Reals complete.

65, TITLE:      Learning to Adapt Structured Output Space for Semantic Segmentation
http://arxiv.org/abs/1802.10349
AUTHORS:        Yi-Hsuan Tsai ; Wei-Chih Hung ; Samuel Schulter ; Kihyuk Sohn ; Ming-Hsuan Yang ; Manmohan Chandraker
COMMENTS:       Accepted in CVPR'18. Code and model are available at https://github.com/wasidennis/AdaptSegNet
HIGHLIGHT:      In this paper, we propose an adversarial learning method for domain adaptation in the context of semantic segmentation.

66, TITLE:      Towards Incorporating Contextual Knowledge into the Prediction of Driving Behavior
http://arxiv.org/abs/2006.08470
AUTHORS:        Florian Wirthmüller ; Julian Schlechtriemen ; Jochen Hipp ; Manfred Reichert
COMMENTS:       the article has been accepted for publication during the 23rd IEEE Intelligent Transportation Systems Conference (ITSC), 7 pages, 6 figures, 1 table
HIGHLIGHT:      Extending former studies, we investigate how predictions are affected by external conditions.

67, TITLE:      Discovering Representations for Black-box Optimization
http://arxiv.org/abs/2003.04389
AUTHORS:        Adam Gaier ; Alexander Asteroth ; Jean-Baptiste Mouret
COMMENTS:       Presented at GECCO 2020 -- v2 (Previous title 'Automating Representation Discovery with MAP-Elites')
HIGHLIGHT:      Our main insight is that this process can be automated by generating a dataset of high-performing solutions with a quality diversity algorithm (here, MAP-Elites), then learning a representation with a generative model (here, a Variational Autoencoder) from that dataset.

68, TITLE:      Practical sensorless aberration estimation for 3D microscopy with deep learning
http://arxiv.org/abs/2006.01804
AUTHORS:        Debayan Saha ; Uwe Schmidt ; Qinrong Zhang ; Aurelien Barbotin ; Qi Hu ; Na Ji ; Martin J. Booth ; Martin Weigert ; Eugene W. Myers
HIGHLIGHT:      Here, we demonstrate that neural networks trained only on simulated data yield accurate predictions for real experimental images.

69, TITLE:      Let's be Humorous: Knowledge Enhanced Humor Generation
http://arxiv.org/abs/2004.13317
AUTHORS:        Hang Zhang ; Dayiheng Liu ; Jiancheng Lv ; Cheng Luo
HIGHLIGHT:      In this paper, we explore how to generate a punchline given the set-up with the relevant knowledge. Furthermore, we create the first humor-knowledge dataset.

70, TITLE:      Causal Modeling for Fairness in Dynamical Systems
http://arxiv.org/abs/1909.09141
AUTHORS:        Elliot Creager ; David Madras ; Toniann Pitassi ; Richard Zemel
HIGHLIGHT:      We discuss causal directed acyclic graphs (DAGs) as a unifying framework for the recent literature on fairness in such dynamical systems.

71, TITLE:      Adversarial T-shirt! Evading Person Detectors in A Physical World
http://arxiv.org/abs/1910.11099
AUTHORS:        Kaidi Xu ; Gaoyuan Zhang ; Sijia Liu ; Quanfu Fan ; Mengshu Sun ; Hongge Chen ; Pin-Yu Chen ; Yanzhi Wang ; Xue Lin
HIGHLIGHT:      In this work, we proposed adversarial T-shirts, a robust physical adversarial example for evading person detectors even if it could undergo non-rigid deformation due to a moving person's pose changes.

72, TITLE:      Optimal Transport, CycleGAN, and Penalized LS for Unsupervised Learning in Inverse Problems
http://arxiv.org/abs/1909.12116
AUTHORS:        Byeongsu Sim ; Gyutaek Oh ; Jeongsol Kim ; Chanyong Jung ; Jong Chul Ye
HIGHLIGHT:      In this article, we provide an important advance to unveil the missing link.

73, TITLE:      BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecture
http://arxiv.org/abs/2007.00243
AUTHORS:        Tiange Xiang ; Chaoyi Zhang ; Dongnan Liu ; Yang Song ; Heng Huang ; Weidong Cai
COMMENTS:       10 pages, 4 figures, MICCAI2020
HIGHLIGHT:      To tackle this issue in such U-Net variants, in this paper, we present a novel Bi-directional O-shape network (BiO-Net) that reuses the building blocks in a recurrent manner without introducing any extra parameters.

74, TITLE:      Hinting Semantic Parsing with Statistical Word Sense Disambiguation
http://arxiv.org/abs/2006.15942
AUTHORS:        Ritwik Bose ; Siddharth Vashishtha ; James Allen
COMMENTS:       Longer version of AAAI2020 student abstract
HIGHLIGHT:      In this work, we provide hints from a statistical WSD system to guide a logical semantic parser to produce better semantic type assignments while maintaining the soundness of the resulting logical forms.

75, TITLE:      Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow
http://arxiv.org/abs/2002.02547
AUTHORS:        Didrik Nielsen ; Ole Winther
HIGHLIGHT:      In this paper, we introduce subset flows, a class of flows that can tractably transform finite volumes and thus allow exact computation of likelihoods for discrete data.

76, TITLE:      Interpretable and Generalizable Person Re-identification with Query-adaptive Convolution and Temporal Lifting
http://arxiv.org/abs/1904.10424
AUTHORS:        Shengcai Liao ; Ling Shao
COMMENTS:       This paper is accepted by ECCV 2020. Project page: https://github.com/ShengcaiLiao/QAConv
HIGHLIGHT:      In this paper, beyond representation learning, we consider how to formulate person image matching directly in deep feature maps.

77, TITLE:      HGKT : Introducing Problem Schema with Hierarchical Exercise Graph for Knowledge Tracing
http://arxiv.org/abs/2006.16915
AUTHORS:        Hanshuang Tong ; Yun Zhou ; Zhen Wang
COMMENTS:       9 pages, 9 figures, submission to the 29th ACM International Conference on Information and Knowledge Management (CIKM '20)
HIGHLIGHT:      Thus, in this paper, we propose a hierarchical graph knowledge tracing model framework (HGKT) which could leverage the advantages of hierarchical exercise graph and sequence model to enhance the ability of knowledge tracing.

78, TITLE:      VisualPhishNet: Zero-Day Phishing Website Detection by Visual Similarity
http://arxiv.org/abs/1909.00300
AUTHORS:        Sahar Abdelnabi ; Katharina Krombholz ; Mario Fritz
HIGHLIGHT:      This paper contributes VisualPhishNet, a new similarity-based phishing detection framework, based on a triplet Convolutional Neural Network (CNN).

79, TITLE:      Revealing Stable and Unstable Modes of Generic Denoisers through Nonlinear Eigenvalue Analysis
http://arxiv.org/abs/1909.12775
AUTHORS:        Ester Hait-Fraenkel ; Guy Gilboa
HIGHLIGHT:      In this paper, we propose to analyze stable and unstable modes of generic image denoisers through nonlinear eigenvalue analysis.

80, TITLE:      Lightweight Temporal Self-Attention for Classifying Satellite Image Time Series
http://arxiv.org/abs/2007.00586
AUTHORS:        Vivien Sainte Fare Garnot ; Loic Landrieu
HIGHLIGHT:      Building on recent work employing multi-headed self-attention mechanisms to classify remote sensing time sequences, we propose a modification of the Temporal Attention Encoder.

81, TITLE:      Adversarial Ranking Attack and Defense
http://arxiv.org/abs/2002.11293
AUTHORS:        Mo Zhou ; Zhenxing Niu ; Le Wang ; Qilin Zhang ; Gang Hua
COMMENTS:       ECCV2020
HIGHLIGHT:      In this paper, we propose two attacks against deep ranking systems, i.e., Candidate Attack and Query Attack, that can raise or lower the rank of chosen candidates by adversarial perturbations.

82, TITLE:      Pedestrian Detection: The Elephant In The Room
http://arxiv.org/abs/2003.08799
AUTHORS:        Irtiza Hasan ; Shengcai Liao ; Jinpeng Li ; Saad Ullah Akram ; Ling Shao
COMMENTS:       17 pages, 1 figure
HIGHLIGHT:      To this end, we conduct a comprehensive study in this paper, using a general principle of direct cross-dataset evaluation.

83, TITLE:      Improving Non-autoregressive Neural Machine Translation with Monolingual Data
http://arxiv.org/abs/2005.00932
AUTHORS:        Jiawei Zhou ; Phillip Keung
COMMENTS:       Published in ACL 2020
HIGHLIGHT:      Under this framework, we leverage large monolingual corpora to improve the NAR model's performance, with the goal of transferring the AR model's generalization ability while preventing overfitting.

84, TITLE:      Dynamic Prosody Generation for Speech Synthesis using Linguistics-Driven Acoustic Embedding Selection
http://arxiv.org/abs/1912.00955
AUTHORS:        Shubhi Tyagi ; Marco Nicolis ; Jonas Rohnke ; Thomas Drugman ; Jaime Lorenzo-Trueba
COMMENTS:       Submitted for INTERSPEECH 2020
HIGHLIGHT:      We propose a novel embedding selection approach which exploits linguistic information, leveraging the speech variability present in the training dataset.

85, TITLE:      Guessing State Tracking for Visual Dialogue
http://arxiv.org/abs/2002.10340
AUTHORS:        Wei Pang ; Xiaojie Wang
COMMENTS:       Accepted at ECCV 2020. More details can be found at https://github.com/xubuvd/guesswhat
HIGHLIGHT:      This paper proposes a guessing state for the Guesser, and regards guess as a process with change of guessing state through a dialogue.

86, TITLE:      Multidimensional ground reaction forces and moments from wearable sensor accelerations via deep learning
http://arxiv.org/abs/1903.07221
AUTHORS:        William R. Johnson ; Ajmal Mian ; Mark A. Robinson ; Jasper Verheul ; David G. Lloyd ; Jacqueline A. Alderson
HIGHLIGHT:      The goal to accurately estimate near real-time on-field GRF/M will be improved by the lessons learned in this study [truncated].

87, TITLE:      TICO-19: the Translation Initiative for Covid-19
http://arxiv.org/abs/2007.01788
AUTHORS:        Antonios Anastasopoulos ; Alessandro Cattelan ; Zi-Yi Dou ; Marcello Federico ; Christian Federman ; Dmitriy Genzel ; Francisco Guzmán ; Junjie Hu ; Macduff Hughes ; Philipp Koehn ; Rosie Lazar ; Will Lewis ; Graham Neubig ; Mengmeng Niu ; Alp Öktem ; Eric Paquin ; Grace Tang ; Sylwia Tur
HIGHLIGHT:      TICO-19: the Translation Initiative for Covid-19

88, TITLE:      AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
http://arxiv.org/abs/2006.08198
AUTHORS:        Yonggan Fu ; Wuyang Chen ; Haotao Wang ; Haoran Li ; Yingyan Lin ; Zhangyang Wang
COMMENTS:       Accepted at ICML2020
HIGHLIGHT:      Inspired by the recent success of AutoML in deep compression, we introduce AutoML to GAN compression and develop an AutoGAN-Distiller (AGD) framework.

89, TITLE:      Deep Fashion3D: A Dataset and Benchmark for 3D Garment Reconstruction from Single Images
http://arxiv.org/abs/2003.12753
AUTHORS:        Heming Zhu ; Yu Cao ; Hang Jin ; Weikai Chen ; Dong Du ; Zhangye Wang ; Shuguang Cui ; Xiaoguang Han
COMMENTS:       23 pages, 9 figures. For project page, see https://kv2000.github.io/2020/03/25/deepFashion3DRevisited/
HIGHLIGHT:      To demonstrate the advantage of Deep Fashion3D, we propose a novel baseline approach for single-view garment reconstruction, which leverages the merits of both mesh and implicit representations.

90, TITLE:      Age-Oriented Face Synthesis with Conditional Discriminator Pool and Adversarial Triplet Loss
http://arxiv.org/abs/2007.00792
AUTHORS:        Haoyi Wang ; Victor Sanchez ; Chang-Tsun Li
HIGHLIGHT:      In this paper, we propose a method for the age-oriented face synthesis task that achieves a high synthesis accuracy with strong identity permanence capabilities.

91, TITLE:      A Baseline for 3D Multi-Object Tracking
http://arxiv.org/abs/1907.03961
AUTHORS:        Xinshuo Weng ; Kris Kitani
COMMENTS:       metrics updated, under submission
HIGHLIGHT:      In contrast, this work proposes a simple yet accurate real-time 3D MOT system.

92, TITLE:      Understanding More about Human and Machine Attention in Deep Neural Networks
http://arxiv.org/abs/1906.08764
AUTHORS:        Qiuxia Lai ; Salman Khan ; Yongwei Nie ; Jianbing Shen ; Hanqiu Sun ; Ling Shao
COMMENTS:       Q. Lai, S. Khan, Y. Nie, J. Shen, H. Sun, L. Shao, TMM, in press, 2020
HIGHLIGHT:      In view of these conflicting evidence, here we make a systematic study on using artificial attention and human attention in neural network design.

93, TITLE:      Too many cooks: Bayesian inference for coordinating multi-agent collaboration
http://arxiv.org/abs/2003.11778
AUTHORS:        Rose E. Wang ; Sarah A. Wu ; James A. Evans ; Joshua B. Tenenbaum ; David C. Parkes ; Max Kleiman-Weiner
COMMENTS:       Rose E. Wang and Sarah A. Wu contributed equally
HIGHLIGHT:      Here, we develop Bayesian Delegation, a decentralized multi-agent learning mechanism with these abilities.

94, TITLE:      Medical Image Segmentation via Unsupervised Convolutional Neural Network
http://arxiv.org/abs/2001.10155
AUTHORS:        Junyu Chen ; Eric C. Frey
HIGHLIGHT:      In this paper, we present a novel learning-based segmentation model that could be trained semi- or un- supervised.