2020.04.23.txt

==========New Papers==========
1, TITLE:       Graph-based Kinship Reasoning Network
http://arxiv.org/abs/2004.10375
AUTHORS:        Wanhua Li ; Yingqiang Zhang ; Kangchen Lv ; Jiwen Lu ; Jianjiang Feng ; Jie Zhou
COMMENTS:       Accepted to ICME 2020(IEEE International Conference on Multimedia & Expo 2020) as an Oral Presentation
HIGHLIGHT:      In this paper, we propose a graph-based kinship reasoning (GKR) network for kinship verification, which aims to effectively perform relational reasoning on the extracted features of an image pair.

2, TITLE:       Automatic exposure selection and fusion for high-dynamic-range photography via smartphones
http://arxiv.org/abs/2004.10365
AUTHORS:        Reza Pourreza ; Nasser Kehtarnavaz
HIGHLIGHT:      In this paper, a method for automatically selecting the exposure settings of such images is introduced based on the camera characteristic function.

3, TITLE:       Yoga-82: A New Dataset for Fine-grained Classification of Human Poses
http://arxiv.org/abs/2004.10362
AUTHORS:        Manisha Verma ; Sudhakar Kumawat ; Yuta Nakashima ; Shanmuganathan Raman
COMMENTS:       Accepted CVPR Workshops 2020
HIGHLIGHT:      To handle more variety in human poses, we propose the concept of fine-grained hierarchical pose classification, in which we formulate the pose estimation as a classification task, and propose a dataset, Yoga-82, for large-scale yoga pose recognition with 82 classes.

4, TITLE:       Testing Machine Translation via Referential Transparency
http://arxiv.org/abs/2004.10361
AUTHORS:        Pinjia He ; Clara Meister ; Zhendong Su
HIGHLIGHT:      To address this problem, we introduce referentially transparent inputs (RTIs), a simple, widely applicable methodology for validating machine translation software.

5, TITLE:       Hierarchically Fair Federated Learning
http://arxiv.org/abs/2004.10386
AUTHORS:        Jingfeng Zhang ; Cheng Li ; Antonio Robles-Kelly ; Mohan Kankanhalli
HIGHLIGHT:      To achieve this, we propose a novel hierarchically fair federated learning (HFFL) framework.

6, TITLE:       Image Processing Failure and Deep Learning Success in Lawn Measurement
http://arxiv.org/abs/2004.10382
AUTHORS:        J. Wilkins ; M. V. Nguyen ; B. Rahmani
HIGHLIGHT:      We used Keras and TensorFlow to estimate the lawn area.

7, TITLE:       Efficient adjustment sets in causal graphical models with hidden variables
http://arxiv.org/abs/2004.10521
AUTHORS:        Ezequiel Smucler ; Facundo Sapienza ; Andrea Rotnitzky
HIGHLIGHT:      We provide polynomial time algorithms to compute the globally optimal (when it exists), optimal minimal, and optimal minimum adjustment sets.

8, TITLE:       Scaling through abstractions -- high-performance vectorial wave simulations for seismic inversion with Devito
http://arxiv.org/abs/2004.10519
AUTHORS:        Mathias Louboutin ; Fabio Luporini ; Philipp Witte ; Rhodri Nelson ; George Bisbas ; Jan Thorbecke ; Felix J. Herrmann ; Gerard Gorman
COMMENTS:       11 pages, 3 figures
HIGHLIGHT:      In this article, the generation and simulation of MPI-parallel propagators (along with their adjoints) for the pseudo-acoustic wave-equation in tilted transverse isotropic media and the elastic wave-equation are presented.

9, TITLE:       Human and Machine Action Prediction Independent of Object Information
http://arxiv.org/abs/2004.10518
AUTHORS:        Fatemeh Ziaeetabar ; Jennifer Pomp ; Stefan Pfeiffer ; Nadiya El-Sourani ; Ricarda I. Schubotz ; Minija Tamosiunaite ; Florentin Wörgötter
COMMENTS:       This paper includes 31 pages, 11 figures and 1 table
HIGHLIGHT:      We employed a computational model -an enriched Semantic Event Chain (eSEC)- incorporating the information of spatial relations, specifically (a) objects' touching/untouching, (b) static spatial relations between objects and (c) dynamic spatial relations between objects.

10, TITLE:      Multi-Domain Learning and Identity Mining for Vehicle Re-Identification
http://arxiv.org/abs/2004.10547
AUTHORS:        Shuting He ; Hao Luo ; Weihua Chen ; Miao Zhang ; Yuqi Zhang ; Fan Wang ; Hao Li ; Wei Jiang
COMMENTS:       Solution for AI City Challenge, CVPR2020 Workshop. Codes are at https://github.com/heshuting555/AICITY2020_DMT_VehicleReID
HIGHLIGHT:      This paper introduces our solution for the Track2 in AI City Challenge 2020 (AICITY20).

11, TITLE:      Structured Mechanical Models for Robot Learning and Control
http://arxiv.org/abs/2004.10301
AUTHORS:        Jayesh K. Gupta ; Kunal Menda ; Zachary Manchester ; Mykel J. Kochenderfer
COMMENTS:       First two authors contributed equally. Accepted at L4DC2020. Source code and videos at https://sites.google.com/stanford.edu/smm/
HIGHLIGHT:      The goal of this work is to demonstrate the benefits of using Structured Mechanical Models in lieu of black-box neural networks when modeling robot dynamics.

12, TITLE:      TetraTSDF: 3D human reconstruction from a single image with a tetrahedral outer shell
http://arxiv.org/abs/2004.10534
AUTHORS:        Hayato Onizuka ; Zehra Hayirci ; Diego Thomas ; Akihiro Sugimoto ; Hideaki Uchiyama ; Rin-ichiro Taniguchi
HIGHLIGHT:      In this paper, we propose the tetrahedral outer shell volumetric truncated signed distance function (TetraTSDF) model for the human body, and its corresponding part connection network (PCN) for 3D human body shape regression.

13, TITLE:      Up or Down? Adaptive Rounding for Post-Training Quantization
http://arxiv.org/abs/2004.10568
AUTHORS:        Markus Nagel ; Rana Ali Amjad ; Mart van Baalen ; Christos Louizos ; Tijmen Blankevoort
HIGHLIGHT:      In this paper, we propose AdaRound, a better weight-rounding mechanism for post-training quantization that adapts to the data and the task loss.

14, TITLE:      Multi-view Self-Constructing Graph Convolutional Networks with Adaptive Class Weighting Loss for Semantic Segmentation
http://arxiv.org/abs/2004.10327
AUTHORS:        Qinghui Liu ; Michael Kampffmeyer ; Robert Jenssen ; Arnt-Børre Salberg
COMMENTS:       7-page, MSCG-Net, CVPRW-2020
HIGHLIGHT:      We propose a novel architecture called the Multi-view Self-Constructing Graph Convolutional Networks (MSCG-Net) for semantic segmentation.

15, TITLE:      Efficient Neighbourhood Consensus Networks via Submanifold Sparse Convolutions
http://arxiv.org/abs/2004.10566
AUTHORS:        Ignacio Rocco ; Relja Arandjelović ; Josef Sivic
HIGHLIGHT:      In this work we target the problem of estimating accurately localised correspondences between a pair of images.

16, TITLE:      A Deep Learning System for Sentiment Analysis of Service Calls
http://arxiv.org/abs/2004.10320
AUTHORS:        Yanan Jia ; Sony SungChu
COMMENTS:       10 pages, 3 figures
HIGHLIGHT:      In this paper, a sentiment analysis pipeline is first carried out with respect to real-world multi-party conversations - that is, service calls.

17, TITLE:      Real-time Simultaneous 3D Head Modeling and Facial Motion Capture with an RGB-D camera
http://arxiv.org/abs/2004.10557
AUTHORS:        Diego Thomas
HIGHLIGHT:      We propose a method to build in real-time animated 3D head models using a consumer-grade RGB-D camera.

18, TITLE:      Combining Deep Learning Classifiers for 3D Action Recognition
http://arxiv.org/abs/2004.10314
AUTHORS:        Jan Sedmidubsky ; Pavel Zezula
COMMENTS:       Submitted to Pattern Recognition Letters
HIGHLIGHT:      In this paper, we propose to train an independent classifier for each available pre-processing technique and fuse the classification results based on a strict majority vote rule.

19, TITLE:      Evolving Dyadic Strategies for a Cooperative Physical Task
http://arxiv.org/abs/2004.10558
AUTHORS:        Saber Sheybani ; Eduardo J. Izquierdo ; Eatai Roth
COMMENTS:       6 pages, 4 figures, IEEE Haptics Symposium 2020
HIGHLIGHT:      Using a genetic algorithm, we evolve simulated agents to explore a space of feasible role-switching policies.

20, TITLE:      Keep It Real: a Window to Real Reality in Virtual Reality
http://arxiv.org/abs/2004.10313
AUTHORS:        Baihan Lin
COMMENTS:       IJCAI 2020
HIGHLIGHT:      This paper proposed a new interaction paradigm in the virtual reality (VR) environments, which consists of a virtual mirror or window projected onto a virtual surface, representing the correct perspective geometry of a mirror or window reflecting the real world.

21, TITLE:      Supervised Grapheme-to-Phoneme Conversion of Orthographic Schwas in Hindi and Punjabi
http://arxiv.org/abs/2004.10353
AUTHORS:        Aryaman Arora ; Luke Gessler ; Nathan Schneider
COMMENTS:       4 pages, 1 figure. To be published in the 2020 Annual Conference of the Association for Computational Linguistics (https://acl2020.org/)
HIGHLIGHT:      We present the first statistical schwa deletion classifier for Hindi, which relies solely on the orthography as the input and outperforms previous approaches.

22, TITLE:      Textual Visual Semantic Dataset for Text Spotting
http://arxiv.org/abs/2004.10349
AUTHORS:        Ahmed Sabir ; Francesc Moreno-Noguer ; Lluís Padró
HIGHLIGHT:      In this paper, we propose a visual context dataset for Text Spotting in the wild, where the publicly available dataset COCO-text [Veit et al. 2016] has been extended with information about the scene (such as objects and places appearing in the image) to enable researchers to include semantic relations between texts and scene in their Text Spotting systems, and to offer a common framework for such approaches.

23, TITLE:      The iWildCam 2020 Competition Dataset
http://arxiv.org/abs/2004.10340
AUTHORS:        Sara Beery ; Elijah Cole ; Arvi Gjoka
COMMENTS:       Fine-Grained Visual Categorization Workshop at CVPR 2020
HIGHLIGHT:      Can we leverage data from other modalities, such as citizen science data and remote sensing data?

24, TITLE:      When and Why is Unsupervised Neural Machine Translation Useless?
http://arxiv.org/abs/2004.10581
AUTHORS:        Yunsu Kim ; Miguel Graça ; Hermann Ney
COMMENTS:       Will appear at EAMT 2020; Extended version of EAMT camera-ready (including appendix)
HIGHLIGHT:      This paper studies the practicality of the current state-of-the-art unsupervised methods in neural machine translation (NMT).

25, TITLE:      How to track your dragon: A Multi-Attentional Framework for real-time RGB-D 6-DOF Object Pose Tracking
http://arxiv.org/abs/2004.10335
AUTHORS:        Isidoros Marougkas ; Petros Koutras ; Nikos Kardaris ; Georgios Retsinas ; Georgia Chalvatzaki ; Petros Maragos
COMMENTS:       5 pages, 6 figures, International Conference of Image Processing (ICIP) 2020
HIGHLIGHT:      We present a novel multi-attentional convolutional architecture to tackle the problem of real-time RGB-D 6D object pose tracking of single, known objects.

26, TITLE:      Discretized Bottleneck in VAE: Posterior-Collapse-Free Sequence-to-Sequence Learning
http://arxiv.org/abs/2004.10603
AUTHORS:        Yang Zhao ; Ping Yu ; Suchismit Mahapatra ; Qinliang Su ; Changyou Chen
HIGHLIGHT:      In this paper, we propose a principled approach to eliminate this issue by applying a discretized bottleneck in the latent space.

27, TITLE:      Contextualised Graph Attention for Improved Relation Extraction
http://arxiv.org/abs/2004.10624
AUTHORS:        Angrosh Mandya ; Danushka Bollegala ; Frans Coenen
HIGHLIGHT:      This paper presents a contextualized graph attention network that combines edge features and multiple sub-graphs for improving relation extraction.

28, TITLE:      R-VGAE: Relational-variational Graph Autoencoder for Unsupervised Prerequisite Chain Learning
http://arxiv.org/abs/2004.10610
AUTHORS:        Irene Li ; Alexander Fabbri ; Swapnil Hingmire ; Dragomir Radev
COMMENTS:       2 Figures, 3 Tables, 9 Pages
HIGHLIGHT:      In this paper, we frame learning prerequisite relationships among concepts as an unsupervised task with no access to labeled concept pairs during training.

29, TITLE:      Stabilizing Training of Generative Adversarial Nets via Langevin Stein Variational Gradient Descent
http://arxiv.org/abs/2004.10495
AUTHORS:        Dong Wang ; Xiaoqian Qin ; Fengyi Song ; Li Cheng
HIGHLIGHT:      In this paper, we propose to stabilize GAN training via a novel particle-based variational inference -- Langevin Stein variational gradient descent (LSVGD), which not only inherits the flexibility and efficiency of original SVGD but aims to address its instability issues by incorporating an extra disturbance into the update dynamics.

30, TITLE:      Differential evolution outside the box
http://arxiv.org/abs/2004.10489
AUTHORS:        Anna V. Kononova ; Fabio Caraffini ; Thomas Bäck
HIGHLIGHT:      Results shown in this study suggest strong dependencies between percentages of generated infeasible solutions and every aspect mentioned above.

31, TITLE:      Understanding Integrated Gradients with SmoothTaylor for Deep Neural Network Attribution
http://arxiv.org/abs/2004.10484
AUTHORS:        Gary S. W. Goh ; Sebastian Lapuschkin ; Leander Weber ; Wojciech Samek ; Alexander Binder
COMMENTS:       8 pages, 3 figures
HIGHLIGHT:      In this paper, we present Smooth Integrated Gradients as a statistically improved attribution method inspired by Taylor's theorem, which does not require a fixed baseline to be chosen.

32, TITLE:      ESPnet-ST: All-in-One Speech Translation Toolkit
http://arxiv.org/abs/2004.10234
AUTHORS:        Hirofumi Inaguma ; Shun Kiyono ; Kevin Duh ; Shigeki Karita ; Nelson Enrique Yalta Soplin ; Tomoki Hayashi ; Shinji Watanabe
COMMENTS:       Accepted at ACL 2020 System Demonstration
HIGHLIGHT:      We present ESPnet-ST, which is designed for the quick development of speech-to-speech translation systems in a single framework.

33, TITLE:      Graph Convolutional Subspace Clustering: A Robust Subspace Clustering Framework for Hyperspectral Image
http://arxiv.org/abs/2004.10476
AUTHORS:        Yaoming Cai ; Zijia Zhang ; Zhihua Cai ; Xiaobo Liu ; Xinwei Jiang ; Qin Yan
COMMENTS:       This paper is submitted to IEEE TGRS
HIGHLIGHT:      In this paper, we revisit the subspace clustering with graph convolution and present a novel subspace clustering framework called Graph Convolutional Subspace Clustering (GCSC) for robust HSI clustering.

34, TITLE:      Learnings from Technological Interventions in a Low Resource Language: A Case-Study on Gondi
http://arxiv.org/abs/2004.10270
AUTHORS:        Devansh Mehta ; Sebastin Santy ; Ramaravind Kommiya Mothilal ; Brij Mohan Lal Srivastava ; Alok Sharma ; Anurag Shukla ; Vishnu Prasad ; Venkanna U ; Amit Sharma ; Kalika Bali
COMMENTS:       Accepted at LREC 2020 (7 pages)
HIGHLIGHT:      In this paper, we report the adoption and deployment of 4 technology-driven methods of data collection for Gondi, a low-resource vulnerable language spoken by around 2.3 million tribal people in south and central India.

35, TITLE:      The Imandra Automated Reasoning System (system description)
http://arxiv.org/abs/2004.10263
AUTHORS:        Grant Olney Passmore ; Simon Cruanes ; Denis Ignatovich ; Dave Aitken ; Matt Bray ; Elijah Kagan ; Kostya Kanishev ; Ewen Maclean ; Nicola Mometto
COMMENTS:       To appear in Proceedings of The International Joint Conference on Automated Reasoning (IJCAR) 2020, Lecture Notes in Artificial Intelligence, Springer-Verlag
HIGHLIGHT:      We describe Imandra, a modern computational logic theorem prover designed to bridge the gap between decision procedures such as SMT, semi-automatic inductive provers of the Boyer-Moore family like ACL2, and interactive proof assistants for typed higher-order logics.

36, TITLE:      Distributed Learning and Inference with Compressed Images
http://arxiv.org/abs/2004.10497
AUTHORS:        Sudeep Katakol ; Basem Elbarashy ; Luis Herranz ; Joost van de Weijer ; Antonio M. Lopez
COMMENTS:       13 pages, 14 figures
HIGHLIGHT:      In this paper, we analyze this phenomenon, with a special focus on vision-based perception for autonomous driving as a paradigmatic scenario.

37, TITLE:      ParaCNN: Visual Paragraph Generation via Adversarial Twin Contextual CNNs
http://arxiv.org/abs/2004.10258
AUTHORS:        Shiyang Yan ; Yang Hua ; Neil Robertson
HIGHLIGHT:      In this paper, we study the visual paragraph generation, which can describe the image with a long paragraph containing rich details.

38, TITLE:      M-LVC: Multiple Frames Prediction for Learned Video Compression
http://arxiv.org/abs/2004.10290
AUTHORS:        Jianping Lin ; Dong Liu ; Houqiang Li ; Feng Wu
COMMENTS:       Accepted to appear in CVPR2020; camera-ready
HIGHLIGHT:      We propose an end-to-end learned video compression scheme for low-latency scenarios.

39, TITLE:      ParkPredict: Motion and Intent Prediction of Vehicles in Parking Lots
http://arxiv.org/abs/2004.10293
AUTHORS:        Xu Shen ; Ivo Batkovic ; Vijay Govindarajan ; Paolo Falcone ; Trevor Darrell ; Francesco Borrelli
COMMENTS:       * Indicates equal contribution. Accepted at IEEE Intelligent Vehicles Symposium (IV) 2020
HIGHLIGHT:      We investigate the problem of predicting driver behavior in parking lots, an environment which is less structured than typical road networks and features complex, interactive maneuvers in a compact space. Using the CARLA simulator, we develop a parking lot environment and collect a dataset of human parking maneuvers.

40, TITLE:      Panoptic-based Image Synthesis
http://arxiv.org/abs/2004.10289
AUTHORS:        Aysegul Dundar ; Karan Sapra ; Guilin Liu ; Andrew Tao ; Bryan Catanzaro
COMMENTS:       CVPR 2020
HIGHLIGHT:      We propose a panoptic aware image synthesis network to generate high fidelity and photorealistic images conditioned on panoptic maps which unify semantic and instance information.

41, TITLE:      Observations on Annotations
http://arxiv.org/abs/2004.10283
AUTHORS:        Georg Rehm
COMMENTS:       To be published in: Annotations in Scholarly Editions and Research: Functions, Differentiation, Systematization (2020), Julia Nantke and Frederik Schlupkothen (editors). De Gruyter. In print
HIGHLIGHT:      This article presents various observations on annotations.

42, TITLE:      Learning Multi-Modal Image Registration without Real Data
http://arxiv.org/abs/2004.10282
AUTHORS:        Malte Hoffmann ; Benjamin Billot ; Juan Eugenio Iglesias ; Bruce Fischl ; Adrian V. Dalca
COMMENTS:       12 pages, 8 figures, deformable image registration, modality independence, training without data
HIGHLIGHT:      We introduce a learning-based strategy for multi-modal registration of images acquired with any modality, without requiring real data during training.

43, TITLE:      Group Activity Detection from Trajectory and Video Data in Soccer
http://arxiv.org/abs/2004.10299
AUTHORS:        Ryan Sanford ; Siavash Gorji ; Luiz G. Hafemann ; Bahareh Pourbabaee ; Mehrsan Javan
COMMENTS:       Accepted to the 6th International Workshop on Computer Vision in Sports (CVsports) at CVPR 2020
HIGHLIGHT:      For this, we propose self-attention models to learn and extract relevant information from a group of soccer players for activity detection from both trajectory and video data.

44, TITLE:      AmbigQA: Answering Ambiguous Open-domain Questions
http://arxiv.org/abs/2004.10645
AUTHORS:        Sewon Min ; Julian Michael ; Hannaneh Hajishirzi ; Luke Zettlemoyer
HIGHLIGHT:      In this paper, we introduce AmbigQA, a new open-domain question answering task which involves predicting a set of question-answer pairs, where every plausible answer is paired with a disambiguated rewrite of the original question.

45, TITLE:      Recursive Social Behavior Graph for Trajectory Prediction
http://arxiv.org/abs/2004.10402
AUTHORS:        Jianhua Sun ; Qinhong Jiang ; Cewu Lu
HIGHLIGHT:      In this paper, we present a novel insight of group-based social interaction model to explore relationships among pedestrians.

46, TITLE:      Logical Natural Language Generation from Open-Domain Tables
http://arxiv.org/abs/2004.10404
AUTHORS:        Wenhu Chen ; Jianshu Chen ; Yu Su ; Zhiyu Chen ; William Yang Wang
COMMENTS:       Accepted to ACL 2020 as Long Paper
HIGHLIGHT:      In this paper, we suggest a new NLG task where a model is tasked with generating natural language statements that can be \emph{logically entailed} by the facts in an open-domain semi-structured table.

47, TITLE:      Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning-Based Approach
http://arxiv.org/abs/2004.10641
AUTHORS:        Sara Hosseinzadeh Kassani ; Peyman Hosseinzadeh Kassasni ; Michal J. Wesolowski ; Kevin A. Schneider ; Ralph Deters
HIGHLIGHT:      In this study, we compare popular deep learning-based feature extraction frameworks for automatic COVID-19 classification.

48, TITLE:      Semantic Entity Enrichment by Leveraging Multilingual Descriptions for Link Prediction
http://arxiv.org/abs/2004.10640
AUTHORS:        Genet Asefa Gesese ; Mehwish Alam ; Harald Sack
HIGHLIGHT:      In this position paper, the problem of effectively leveraging multilingual entity descriptions for the purpose of link prediction in KGs will be discussed along with potential solutions to the problem.

49, TITLE:      Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection
http://arxiv.org/abs/2004.10643
AUTHORS:        Joakim Nivre ; Marie-Catherine de Marneffe ; Filip Ginter ; Jan Hajič ; Christopher D. Manning ; Sampo Pyysalo ; Sebastian Schuster ; Francis Tyers ; Daniel Zeman
COMMENTS:       LREC 2020
HIGHLIGHT:      In this paper, we describe version 2 of the guidelines (UD v2), discuss the major changes from UD v1 to UD v2, and give an overview of the currently available treebanks for 90 languages.

50, TITLE:      Unpaired Photo-to-manga Translation Based on The Methodology of Manga Drawing
http://arxiv.org/abs/2004.10634
AUTHORS:        Hao Su ; Jianwei Niu ; Xuefeng Liu ; Qingfeng Li ; Jiahe Cui ; Ji Wan
COMMENTS:       17 pages
HIGHLIGHT:      In this paper, we propose MangaGAN, the first method based on Generative Adversarial Network (GAN) for unpaired photo-to-manga translation. For training MangaGAN, we construct a new dataset collected from a popular manga work, containing manga facial features, landmarks, bodies, and so on.

51, TITLE:      Discovering Imperfectly Observable Adversarial Actions using Anomaly Detection
http://arxiv.org/abs/2004.10638
AUTHORS:        Olga Petrova ; Karel Durkota ; Galina Alperovich ; Karel Horak ; Michal Najman ; Branislav Bosansky ; Viliam Lisy
COMMENTS:       9 pages, 3 figures, 3 tables. Extended Abstract of this paper is accepted to AAMAS 2020
HIGHLIGHT:      We propose two algorithms for solving such games -- a direct extension of existing algorithms based on discretizing the feature space and linear programming and the second algorithm based on constrained learning.

52, TITLE:      Policy Gradient from Demonstration and Curiosity
http://arxiv.org/abs/2004.10430
AUTHORS:        Jie Chen ; Wenjun Xu
HIGHLIGHT:      In this work, an integrated policy gradient algorithm was proposed to boost exploration and facilitate intrinsic reward learning from only limited number of demonstrations.

53, TITLE:      Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem
http://arxiv.org/abs/2004.10424
AUTHORS:        Vahid Roostapour ; Jakob Bossek ; Frank Neumann
COMMENTS:       To be presented at GECCO 2020
HIGHLIGHT:      We present example graphs where the biased mutation can significantly speed up the expected runtime until (Pareto-)optimal solutions are found.

54, TITLE:      Dialogue State Tracking with Pretrained Encoder for Multi-domain Trask-oriented Dialogue Systems
http://arxiv.org/abs/2004.10663
AUTHORS:        Dingmin Wang ; Chenghua Lin ; Li Zhong ; Kam-Fai Wong
HIGHLIGHT:      In this work, we present a novel architecture, which decomposes the DST task into three sub-tasks to jointly extract dialogue states.

55, TITLE:      A review: Deep learning for medical image segmentation using multi-modality fusion
http://arxiv.org/abs/2004.10664
AUTHORS:        Tongxue Zhou ; Su Ruan ; Stéphane Canu
COMMENTS:       26 pages, 8 figures
HIGHLIGHT:      In this paper, we give an overview of deep learning-based approaches for multi-modal medical image segmentation task.

56, TITLE:      Trading Off Diversity and Quality in Natural Language Generation
http://arxiv.org/abs/2004.10450
AUTHORS:        Hugh Zhang ; Daniel Duckworth ; Daphne Ippolito ; Arvind Neelakantan
HIGHLIGHT:      We address these issues by casting decoding as a multi-objective optimization problem aiming to simultaneously maximize both response quality and diversity.

57, TITLE:      DyNet: Dynamic Convolution for Accelerating Convolutional Neural Networks
http://arxiv.org/abs/2004.10694
AUTHORS:        Yikang Zhang ; Jian Zhang ; Qiang Wang ; Zhao Zhong
HIGHLIGHT:      To address this issue, we propose a novel dynamic convolution method to adaptively generate convolution kernels based on image contents.

58, TITLE:      Learning an Adaptive Model for Extreme Low-light Raw Image Processing
http://arxiv.org/abs/2004.10447
AUTHORS:        Qingxu Fu ; Xiaoguang Di ; Yu Zhang
HIGHLIGHT:      In this work, we propose an adaptive low-light raw image enhancement network to avoid parameter-handcrafting and to improve image quality.

59, TITLE:      DeepFake Detection by Analyzing Convolutional Traces
http://arxiv.org/abs/2004.10448
AUTHORS:        Luca Guarnera ; Oliver Giudice ; Sebastiano Battiato
HIGHLIGHT:      In this work we focus on the analysis of Deepfakes of human faces with the objective of creating a new detection method able to detect a forensics trace hidden in images: a sort of fingerprint left in the image generation process.

60, TITLE:      Pseudo RGB-D for Self-Improving Monocular SLAM and Depth Prediction
http://arxiv.org/abs/2004.10681
AUTHORS:        Lokender Tiwari ; Pan Ji ; Quoc-Huy Tran ; Bingbing Zhuang ; Saket Anand ; Manmohan Chandraker
COMMENTS:       24 pages
HIGHLIGHT:      In this paper, we demonstrate that the coupling of these two by leveraging the strengths of each mitigates the other's shortcomings.

61, TITLE:      Tactical Decision-Making in Autonomous Driving by Reinforcement Learning with Uncertainty Estimation
http://arxiv.org/abs/2004.10439
AUTHORS:        Carl-Johan Hoel ; Krister Wolff ; Leo Laine
HIGHLIGHT:      In this study, the estimated uncertainty is used to choose safe actions in unknown situations.

62, TITLE:      Where is the context? -- A critique of recent dialogue datasets
http://arxiv.org/abs/2004.10473
AUTHORS:        Johannes E. M. Mosig ; Vladimir Vlasov ; Alan Nichol
HIGHLIGHT:      We identify several issues with the above-mentioned datasets, such as history independence, strong knowledge base dependence, and ambiguous system responses.

63, TITLE:      Warwick Image Forensics Dataset for Device Fingerprinting In Multimedia Forensics
http://arxiv.org/abs/2004.10469
AUTHORS:        Yijun Quan ; Chang-Tsun Li ; Yujue Zhou ; Li Li
COMMENTS:       Paper accepted to IEEE International Conference on Multimedia and Expo 2020 (ICME 2020)
HIGHLIGHT:      In this paper, we present the Warwick Image Forensics Dataset, an image dataset of more than 58,600 images captured using 14 digital cameras with various exposure settings.

64, TITLE:      Partial Volume Segmentation of Brain MRI Scans of any Resolution and Contrast
http://arxiv.org/abs/2004.10221
AUTHORS:        Benjamin Billot ; Eleanor D. Robinson ; Adrian V. Dalca ; Juan Eugenio Iglesias
COMMENTS:       10 pages, 7 figures
HIGHLIGHT:      In this work, we present PV-SynthSeg, a convolutional neural network (CNN) that tackles this problem by directly learning a mapping between (possibly multi-modal) low resolution (LR) scans and underlying high resolution (HR) segmentations.

65, TITLE:      Keyphrase Prediction With Pre-trained Language Model
http://arxiv.org/abs/2004.10462
AUTHORS:        Rui Liu ; Zheng Lin ; Weiping Wang
COMMENTS:       7pages, 3 figures, 3 tables
HIGHLIGHT:      Considering the different characteristics of extractive and generative methods, we propose to divide the keyphrase prediction into two subtasks, i.e., present keyphrase extraction (PKE) and absent keyphrase generation (AKG), to fully exploit their respective advantages.

66, TITLE:      MT-Clinical BERT: Scaling Clinical Information Extraction with Multitask Learning
http://arxiv.org/abs/2004.10220
AUTHORS:        Andriy Mulyar ; Bridget T. McInnes
HIGHLIGHT:      We address these challenges by developing Multitask-Clinical BERT: a single deep learning model that simultaneously performs eight clinical tasks spanning entity extraction, PHI identification, language entailment and similarity by sharing representations amongst tasks.

67, TITLE:      A Study of Non-autoregressive Model for Sequence Generation
http://arxiv.org/abs/2004.10454
AUTHORS:        Yi Ren ; Jinglin Liu ; Xu Tan ; Sheng Zhao ; Zhou Zhao ; Tie-Yan Liu
COMMENTS:       Accepted by ACL 2020
HIGHLIGHT:      To quantify such dependency, we propose an analysis model called CoMMA to characterize the difficulty of different NAR sequence generation tasks.

68, TITLE:      AutoEG: Automated Experience Grafting for Off-Policy Deep Reinforcement Learning
http://arxiv.org/abs/2004.10698
AUTHORS:        Keting Lu ; Shiqi Zhang ; Xiaoping Chen
HIGHLIGHT:      Focusing on addressing this limitation, this paper makes a twofold contribution.

69, TITLE:      Disjoint principal component analysis by constrained binary particle swarm optimization
http://arxiv.org/abs/2004.10701
AUTHORS:        John Ramírez-Figueroa ; Carlos Martín-Barreiro ; Ana B. Nieto-Librero ; Victor Leiva-Sánchez ; Purificación Galindo-Villardón
COMMENTS:       34 pages, 2 figures
HIGHLIGHT:      In this paper, we propose an alternative method to the disjoint principal component analysis.

70, TITLE:      CORD-19: The Covid-19 Open Research Dataset
http://arxiv.org/abs/2004.10706
AUTHORS:        Lucy Lu Wang ; Kyle Lo ; Yoganand Chandrasekhar ; Russell Reas ; Jiangjiang Yang ; Darrin Eide ; Kathryn Funk ; Rodney Kinney ; Ziyang Liu ; William Merrill ; Paul Mooney ; Dewey Murdick ; Devvret Rishi ; Jerry Sheehan ; Zhihong Shen ; Brandon Stilson ; Alex D. Wade ; Kuansan Wang ; Chris Wilhelm ; Boya Xie ; Douglas Raymond ; Daniel S. Weld ; Oren Etzioni ; Sebastian Kohlmeier
COMMENTS:       10 pages, 3 figures
HIGHLIGHT:      In this article, we describe the mechanics of dataset construction, highlighting challenges and key design decisions, provide an overview of how CORD-19 has been used, and preview tools and upcoming shared tasks built around the dataset.

71, TITLE:      Chip Placement with Deep Reinforcement Learning
http://arxiv.org/abs/2004.10746
AUTHORS:        Azalia Mirhoseini ; Anna Goldie ; Mustafa Yazgan ; Joe Jiang ; Ebrahim Songhori ; Shen Wang ; Young-Joon Lee ; Eric Johnson ; Omkar Pathak ; Sungmin Bae ; Azade Nazi ; Jiwoo Pak ; Andy Tong ; Kavya Srinivasa ; William Hang ; Emre Tuncer ; Anand Babu ; Quoc V. Le ; James Laudon ; Richard Ho ; Roger Carpenter ; Jeff Dean
HIGHLIGHT:      In this work, we present a learning-based approach to chip placement, one of the most complex and time-consuming stages of the chip design process.

72, TITLE:      Categories of Semantic Concepts
http://arxiv.org/abs/2004.10741
AUTHORS:        James Hefford ; Vincent Wang ; Matthew Wilson
HIGHLIGHT:      Modelling concept representation is a foundational problem in the study of cognition and linguistics.

73, TITLE:      Frechet-Like Distances between Two Merge Trees
http://arxiv.org/abs/2004.10747
AUTHORS:        Elena Farahbakhsh Touli
HIGHLIGHT:      The purpose of this paper is to extend the definition of Frechet distance which measures the distance between two curves to a distance (Frechet-Like distance) which measures the similarity between two rooted trees.

74, TITLE:      Deep Learning for Screening COVID-19 using Chest X-Ray Images
http://arxiv.org/abs/2004.10507
AUTHORS:        Sanhita Basu ; Sushmita Mitra
HIGHLIGHT:      Therefore, we propose a new concept called domain extension transfer learning (DETL).

75, TITLE:      Red-GAN: Attacking class imbalance via conditioned generation. Yet another medical imaging perspective
http://arxiv.org/abs/2004.10734
AUTHORS:        Ahmad B Qasim ; Ivan Ezhov ; Suprosanna Shit ; Oliver Schoppe ; Johannes C Paetzold ; Anjany Sekuboyina ; Florian Kofler ; Jana Lipkova ; Hongwei Li ; Bjoern Menze
HIGHLIGHT:      In an attempt to mitigate the problem, we propose a data augmentation protocol based on generative adversarial networks.



==========Updates to Previous Papers==========
1, TITLE:       EKFPnP: Extended Kalman Filter for Camera Pose Estimation in a Sequence of Images
http://arxiv.org/abs/1906.10324
AUTHORS:        Mohammad Amin Mehralian ; Mohsen Soryani
HIGHLIGHT:      In this paper, we consider both the temporal dependency of camera poses and the uncertainty of features for the sequential camera pose estimation.

2, TITLE:       Crowd Counting with Decomposed Uncertainty
http://arxiv.org/abs/1903.07427
AUTHORS:        Min-hwan Oh ; Peder A. Olsen ; Karthikeyan Natesan Ramamurthy
COMMENTS:       Accepted in AAAI 2020 (Main Technical Track)
HIGHLIGHT:      In this work, we focus on uncertainty estimation in the domain of crowd counting.

3, TITLE:       Biological sex classification with structural MRI data shows increased misclassification in transgender women
http://arxiv.org/abs/1911.10617
AUTHORS:        Claas Flint ; Katharina Förster ; Sophie A. Koser ; Carsten Konrad ; Pienie Zwitserlood ; Klaus Berger ; Marco Hermesdorf ; Tilo Kircher ; Igor Nenadic ; Axel Krug ; Bernhard T. Baune ; Katharina Dohm ; Ronny Redlich ; Nils Opel ; Volker Arolt ; Tim Hahn ; Xiaoyi Jiang ; Udo Dannlowski ; Dominik Grotegerd
COMMENTS:       Content adapted to the publication at Neuropsychopharmacology
HIGHLIGHT:      To substantiate evidence that the brain structure of TIs differs from male and female, we use a combined multivariate and univariate approach.

4, TITLE:       Semi-Supervised Learning with Scarce Annotations
http://arxiv.org/abs/1905.08845
AUTHORS:        Sylvestre-Alvise Rebuffi ; Sebastien Ehrhardt ; Kai Han ; Andrea Vedaldi ; Andrew Zisserman
COMMENTS:       Workshop on Deep Vision, CVPR 2020
HIGHLIGHT:      In this work, we consider the problem of SSL multi-class classification with very few labelled instances.

5, TITLE:       Hyper-spectral NIR and MIR data and optimal wavebands for detection of apple tree diseases
http://arxiv.org/abs/2004.02325
AUTHORS:        Dmitrii Shadrin ; Mariia Pukalchik ; Anastasia Uryasheva ; Nikita Rodichenko ; Dzmitry Tsetserukou
COMMENTS:       Paper presented at the ICLR 2020 Workshop on Computer Vision for Agriculture (CV4A)
HIGHLIGHT:      This research proposes a modern approach for analyzing the spectral data in Near-Infrared and Mid-Infrared ranges of the apple tree diseases at different stages.

6, TITLE:       HMANet: Hybrid Multiple Attention Network for Semantic Segmentation in Aerial Images
http://arxiv.org/abs/2001.02870
AUTHORS:        Ruigang Niu ; Xian Sun ; Wenhui Diao ; Kaiqiang Chen ; Kun Fu
HIGHLIGHT:      In this work, we propose a novel attention-based framework named Hybrid Multiple Attention Network (HMANet) to adaptively capture global correlations from the perspective of space, channel and category in a more effective and efficient manner.

7, TITLE:       Content Enhanced BERT-based Text-to-SQL Generation
http://arxiv.org/abs/1910.07179
AUTHORS:        Tong Guo ; Huilin Gao
COMMENTS:       working in progress
HIGHLIGHT:      We present a simple methods to leverage the table content for the BERT-based model to solve the text-to-SQL problem.

8, TITLE:       Decoupling Video and Human Motion: Towards Practical Event Detection in Athlete Recordings
http://arxiv.org/abs/2004.09776
AUTHORS:        Moritz Einfalt ; Rainer Lienhart
COMMENTS:       Accepted at 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshop (CVPRW)
HIGHLIGHT:      In this paper we address the problem of motion event detection in athlete recordings from individual sports.

9, TITLE:       PanNuke Dataset Extension, Insights and Baselines
http://arxiv.org/abs/2003.10778
AUTHORS:        Jevgenij Gamper ; Navid Alemi Koohbanani ; Ksenija Benes ; Simon Graham ; Mostafa Jahanifar ; Syed Ali Khurram ; Ayesha Azam ; Katherine Hewitt ; Nasir Rajpoot
COMMENTS:       Work in progress
HIGHLIGHT:      We study the performance of segmentation and classification models when applied to the proposed dataset and demonstrate the application of models trained on PanNuke to whole-slide images.

10, TITLE:      $P\neq NP$
http://arxiv.org/abs/2003.09791
AUTHORS:        Tianrong Lin
COMMENTS:       It seems that the proofs of Theorem 1.4 and Theorem 1.6 are very very difficulty; To low the difficulty of proofs, we need to change the statements of Theorem 1.4 and Theorem 1.6 to be "there exists $AL\in NP-P$ (resp. $coAL\in coNP-coP$)" but not "for any $L_1\in NP-P$ (resp. $L_1\in coNP-coP$)". The author is proceeding to fix it
HIGHLIGHT:      The main contribution of the present paper is that a series of results are obtained.

11, TITLE:      CoBe -- Coded Beacons for Localization, Object Tracking, and SLAM Augmentation
http://arxiv.org/abs/1708.05625
AUTHORS:        Roman Rabinovich ; Ibrahim Jubran ; Aaron Wetzler ; Ron Kimmel
HIGHLIGHT:      This paper presents a novel beacon light coding protocol, which enables fast and accurate identification of the beacons in an image.

12, TITLE:      Beyond Camera Motion Removing: How to Handle Outliers in Deblurring
http://arxiv.org/abs/2002.10201
AUTHORS:        Meng Chang ; Chenwei Yang ; Huajun Feng ; Zhihai Xu ; Qi Li
HIGHLIGHT:      We propose a salient edge detection network to supervise the training process and solve the outlier problem by proposing a novel method of dataset generation.

13, TITLE:      Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation
http://arxiv.org/abs/2003.08040
AUTHORS:        Zhonghao Wang ; Mo Yu ; Yunchao Wei ; Rogerio Feris ; Jinjun Xiong ; Wen-mei Hwu ; Thomas S. Huang ; Honghui Shi
COMMENTS:       CVPR 2020
HIGHLIGHT:      We consider the problem of unsupervised domain adaptation for semantic segmentation by easing the domain shift between the source domain (synthetic data) and the target domain (real data) in this work.

14, TITLE:      Vispi: Automatic Visual Perception and Interpretation of Chest X-rays
http://arxiv.org/abs/1906.05190
AUTHORS:        Xin Li ; Rui Cao ; Dongxiao Zhu
COMMENTS:       In the proceeding of Medical Imaging with Deep Learning (MIDL-20)
HIGHLIGHT:      To tackle these challenges, we present Vispi, an automatic medical image interpretation system, which first annotates an image via classifying and localizing common thoracic diseases with visual support and then followed by report generation from an attentive LSTM model.

15, TITLE:      Histogram Layers for Texture Analysis
http://arxiv.org/abs/2001.00215
AUTHORS:        Joshua Peeples ; Weihuang Xu ; Alina Zare
COMMENTS:       16 pages, 6 figures
HIGHLIGHT:      We present a histogram layer for artificial neural networks (ANNs).

16, TITLE:      Fast and Regularized Reconstruction of Building Façades from Street-View Images using Binary Integer Programming
http://arxiv.org/abs/2002.08549
AUTHORS:        Han Hu ; Libin Wang ; Mier Zhang ; Yulin Ding ; Qing Zhu
HIGHLIGHT:      Aiming to alleviate this issue, we cast the problem into binary integer programming, which omits the requirements for real value parameters and is more efficient to be solved .

17, TITLE:      Comparison of object detection methods for crop damage assessment using deep learning
http://arxiv.org/abs/1912.13199
AUTHORS:        Ali HamidiSepehr ; Seyed Vahid Mirnezami ; Jason K. Ward
HIGHLIGHT:      The goal of this study was a proof-of-concept to detect damaged crop areas from aerial imagery using computer vision and deep learning techniques.

18, TITLE:      Key Protected Classification for Collaborative Learning
http://arxiv.org/abs/1908.10172
AUTHORS:        Mert Bülent Sarıyıldız ; Ramazan Gökberk Cinbiş ; Erman Ayday
COMMENTS:       Accepted to Pattern Recognition
HIGHLIGHT:      In this work, we propose a novel classification model that is resilient against such attacks by design.

19, TITLE:      Incremental Meta-Learning via Indirect Discriminant Alignment
http://arxiv.org/abs/2002.04162
AUTHORS:        Qing Liu ; Orchid Majumder ; Alessandro Achille ; Avinash Ravichandran ; Rahul Bhotika ; Stefano Soatto
HIGHLIGHT:      Through our experimental setup, we develop a notion of incremental learning during the meta-training phase of meta-learning and propose a method which can be used with multiple existing metric-based meta-learning algorithms.

20, TITLE:      Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning
http://arxiv.org/abs/1906.04585
AUTHORS:        Mahmoud Assran ; Joshua Romoff ; Nicolas Ballas ; Joelle Pineau ; Michael Rabbat
HIGHLIGHT:      We propose Gossip-based Actor-Learner Architectures (GALA) where several actor-learners (such as A2C agents) are organized in a peer-to-peer communication topology, and exchange information through asynchronous gossip in order to take advantage of a large number of distributed simulators.

21, TITLE:      SIBRE: Self Improvement Based REwards for Reinforcement Learning
http://arxiv.org/abs/2004.09846
AUTHORS:        Somjit Nath ; Richa Verma ; Abhik Ray ; Harshad Khadilkar
COMMENTS:       7 pages, 10 figures
HIGHLIGHT:      We propose a generic reward shaping approach for improving rate of convergence in reinforcement learning (RL), called Self Improvement Based REwards, or SIBRE.

22, TITLE:      Tensor completion using enhanced multiple modes low-rank prior and total variation
http://arxiv.org/abs/2004.08747
AUTHORS:        Haijin Zeng ; Xiaozhen Xie ; Jifeng Ning
HIGHLIGHT:      In this paper, we propose a novel model to recover a low-rank tensor by simultaneously performing double nuclear norm regularized low-rank matrix factorizations to the all-mode matricizations of the underlying tensor.

23, TITLE:      Non-Autoregressive Machine Translation with Latent Alignments
http://arxiv.org/abs/2004.07437
AUTHORS:        Chitwan Saharia ; William Chan ; Saurabh Saxena ; Mohammad Norouzi
HIGHLIGHT:      This paper investigates two latent alignment models for non-autoregressive machine translation, namely CTC and Imputer.

24, TITLE:      Learning large logic programs by going beyond entailment
http://arxiv.org/abs/2004.09855
AUTHORS:        Andrew Cropper ; Sebastijan Dumančić
COMMENTS:       IJCAI2020 paper
HIGHLIGHT:      We implement our idea in Brute, a new ILP system which uses best-first search, guided by an example-dependent loss function, to incrementally build programs.

25, TITLE:      Universal Physical Camouflage Attacks on Object Detectors
http://arxiv.org/abs/1909.04326
AUTHORS:        Lifeng Huang ; Chengying Gao ; Yuyin Zhou ; Cihang Xie ; Alan Yuille ; Changqing Zou ; Ning Liu
COMMENTS:       CVPR 2020; codes, models, and demos are available at https://mesunhlf.github.io/index_physical.html
HIGHLIGHT:      In this paper, we study physical adversarial attacks on object detectors in the wild. In order to make UPC effective for non-rigid or non-planar objects, we introduce a set of transformations for mimicking deformable properties. To fairly evaluate the effectiveness of different physical-world attacks, we present the first standardized virtual database, AttackScenes, which simulates the real 3D world in a controllable and reproducible environment.

26, TITLE:      Soft Threshold Weight Reparameterization for Learnable Sparsity
http://arxiv.org/abs/2002.03231
AUTHORS:        Aditya Kusupati ; Vivek Ramanujan ; Raghav Somani ; Mitchell Wortsman ; Prateek Jain ; Sham Kakade ; Ali Farhadi
COMMENTS:       18 pages, 10 figures
HIGHLIGHT:      This work proposes Soft Threshold Reparameterization (STR), a novel use of the soft-threshold operator on DNN weights.

27, TITLE:      Physics-enhanced machine learning for virtual fluorescence microscopy
http://arxiv.org/abs/2004.04306
AUTHORS:        Colin L. Cooke ; Fanjie Kong ; Amey Chaware ; Kevin C. Zhou ; Kanghyun Kim ; Rong Xu ; D. Michael Ando ; Samuel J. Yang ; Pavan Chandra Konda ; Roarke Horstmeyer
COMMENTS:       12 pages, 13 figures
HIGHLIGHT:      This paper introduces a new method of data-driven microscope design for virtual fluorescence microscopy.

28, TITLE:      Neural Image Inpainting Guided with Descriptive Text
http://arxiv.org/abs/2004.03212
AUTHORS:        Lisai Zhang ; Qingcai Chen ; Baotian Hu ; Shuoran Jiang
COMMENTS:       6 pages, 2 tables
HIGHLIGHT:      To acquire more semantically accurate inpainting images, this paper proposes a novel inpainting model named \textit{N}eural \textit{I}mage Inpainting \textit{G}uided with \textit{D}escriptive \textit{T}ext (NIGDT).

29, TITLE:      Imputer: Sequence Modelling via Imputation and Dynamic Programming
http://arxiv.org/abs/2002.08926
AUTHORS:        William Chan ; Chitwan Saharia ; Geoffrey Hinton ; Mohammad Norouzi ; Navdeep Jaitly
HIGHLIGHT:      This paper presents the Imputer, a neural sequence model that generates output sequences iteratively via imputations.

30, TITLE:      An overview of embedding models of entities and relationships for knowledge base completion
http://arxiv.org/abs/1703.08098
AUTHORS:        Dat Quoc Nguyen
COMMENTS:       14 pages, 2 figures and 4 tables
HIGHLIGHT:      This paper serves as a comprehensive overview of embedding models of entities and relationships for knowledge base completion, summarizing up-to-date experimental results on standard benchmark datasets.

31, TITLE:      Hierarchical Clustering with Hard-batch Triplet Loss for Person Re-identification
http://arxiv.org/abs/1910.12278
AUTHORS:        Kaiwei Zeng
COMMENTS:       Accepted by CVPR2020
HIGHLIGHT:      Concretely, we propose a hierarchical clustering-guided re-ID (HCR) method.

32, TITLE:      Recognizing Long Grammatical Sequences Using Recurrent Networks Augmented With An External Differentiable Stack
http://arxiv.org/abs/2004.07623
AUTHORS:        Ankur Mali ; Alexander Ororbia ; Daniel Kifer ; Clyde Lee Giles
COMMENTS:       14 pages, 10 tables
HIGHLIGHT:      In this paper, we improve the memory-augmented RNN with important architectural and state updating mechanisms that ensure that the model learns to properly balance the use of its latent states with external memory.

33, TITLE:      Siamese Box Adaptive Network for Visual Tracking
http://arxiv.org/abs/2003.06761
AUTHORS:        Zedu Chen ; Bineng Zhong ; Guorong Li ; Shengping Zhang ; Rongrong Ji
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      To address this issue, we propose a simple yet effective visual tracking framework (named Siamese Box Adaptive Network, SiamBAN) by exploiting the expressive power of the fully convolutional network (FCN).

34, TITLE:      A fast tunable blurring algorithm for scattered data
http://arxiv.org/abs/1906.06722
AUTHORS:        Gregor Robinson ; Ian Grooms
COMMENTS:       19 pages, 5 figures
HIGHLIGHT:      A blurring algorithm with linear time complexity can reduce the small-scale content of data observed at scattered locations in a spatially extended domain of arbitrary dimension.

35, TITLE:      Compressive MRI quantification using convex spatiotemporal priors and deep auto-encoders
http://arxiv.org/abs/2001.08746
AUTHORS:        Mohammad Golbabaee ; Guido Buonincontri ; Carolin Pirkl ; Marion Menzel ; Bjoern Menze ; Mike Davies ; Pedro Gomez
HIGHLIGHT:      We propose a dictionary-matching-free pipeline for multi-parametric quantitative MRI image computing.

36, TITLE:      An Alternative Probabilistic Interpretation of the Huber Loss
http://arxiv.org/abs/1911.02088
AUTHORS:        Gregory P. Meyer
HIGHLIGHT:      In this work, we propose an alternative probabilistic interpretation of the Huber loss, which relates minimizing the loss to minimizing an upper-bound on the Kullback-Leibler divergence between Laplace distributions where one distribution represents the noise in the ground-truth and the other represents the noise in the prediction.

37, TITLE:      Efficient Logspace Classes for Enumeration, Counting, and Uniform Generation
http://arxiv.org/abs/1906.09226
AUTHORS:        Marcelo Arenas ; Luis Alberto Croquevielle ; Rajesh Jayaram ; Cristian Riveros
COMMENTS:       To appear in PODS 2019
HIGHLIGHT:      In this work, we study two simple yet general complexity classes, based on logspace Turing machines, which provide a unifying framework for efficient query evaluation in areas like information extraction and graph databases, among others.

38, TITLE:      Energy Clustering for Unsupervised Person Re-identification
http://arxiv.org/abs/1909.00112
AUTHORS:        Kaiwei Zeng
COMMENTS:       Accepted by Image and Vision Computing
HIGHLIGHT:      To solve this problem, we propose to use the energy distance to evaluate both the inter-cluster and intra-cluster distance in hierarchical clustering(E-cluster), and use the sum of squares of deviations(SSD) as a regularization term to further balance the diversity and similarity of energy distance evaluation.

39, TITLE:      Gated Convolutional Bidirectional Attention-based Model for Off-topic Spoken Response Detection
http://arxiv.org/abs/2004.09036
AUTHORS:        Yefei Zha ; Ruobing Li ; Hui Lin
COMMENTS:       To appear at ACL 2020 (long paper)
HIGHLIGHT:      In this paper, we propose a novel approach for off-topic spoken response detection with high off-topic recall on both seen and unseen prompts.

40, TITLE:      Few-Shot Learning with Embedded Class Models and Shot-Free Meta Training
http://arxiv.org/abs/1905.04398
AUTHORS:        Avinash Ravichandran ; Rahul Bhotika ; Stefano Soatto
COMMENTS:       Accepted to ICCV 2019
HIGHLIGHT:      We propose a method for learning embeddings for few-shot learning that is suitable for use with any number of ways and any number of shots (shot-free).

41, TITLE:      e-SNLI-VE-2.0: Corrected Visual-Textual Entailment with Natural Language Explanations
http://arxiv.org/abs/2004.03744
AUTHORS:        Virginie Do ; Oana-Maria Camburu ; Zeynep Akata ; Thomas Lukasiewicz
HIGHLIGHT:      e-SNLI-VE-2.0: Corrected Visual-Textual Entailment with Natural Language Explanations

42, TITLE:      Low-Resource Text Classification using Domain-Adversarial Learning
http://arxiv.org/abs/1807.05195
AUTHORS:        Daniel Grießhaber ; Ngoc Thang Vu ; Johannes Maucher
HIGHLIGHT:      Deep learning techniques have recently shown to be successful in many natural language processing tasks forming state-of-the-art systems.

43, TITLE:      Learning Spatial Relationships between Samples of Patent Image Shapes
http://arxiv.org/abs/2004.05713
AUTHORS:        Juan Castorena ; Manish Bhattarai ; Diane Oyen
HIGHLIGHT:      In this work, we propose a method suitable to binary images which bridges some of the successes of deep learning (DL) to alleviate the problems introduced by the aforementioned variations.

44, TITLE:      Mimic The Raw Domain: Accelerating Action Recognition in the Compressed Domain
http://arxiv.org/abs/1911.08206
AUTHORS:        Barak Battash ; Haim Barad ; Hanlin Tang ; Amit Bleiweiss
COMMENTS:       CVPR 2020: Joint Workshop on Efficient Deep Learning in Computer Vision
HIGHLIGHT:      In this paper we are approaching the task in a completely different way; we are looking at the data from the compressed stream as a one unit clip and propose that the residual frames can replace the original RGB frames from the raw domain.

45, TITLE:      Channel-by-Channel Demosaicking Networks with Embedded Spectral Correlation
http://arxiv.org/abs/1906.09884
AUTHORS:        Niu Yan ; Jihong Ouyang
HIGHLIGHT:      This paper proposes an accurate and fast demosaicking model based on Convolutional Neural Networks (CNN) for the Bayer CFA, which is the most popular color filter arrangement adopted by digital camera manufacturers.

46, TITLE:      Turning 30: New Ideas in Inductive Logic Programming
http://arxiv.org/abs/2002.11002
AUTHORS:        Andrew Cropper ; Sebastijan Dumančić ; Stephen H. Muggleton
COMMENTS:       IJCAI2020 survey paper
HIGHLIGHT:      We focus on new methods for learning recursive programs that generalise from few examples, a shift from using hand-crafted background knowledge to \emph{learning} background knowledge, and the use of different technologies, notably answer set programming and neural networks.

47, TITLE:      Neural View-Interpolation for Sparse Light Field Video
http://arxiv.org/abs/1910.13921
AUTHORS:        Mojtaba Bemana ; Karol Myszkowski ; Hans-Peter Seidel ; Tobias Ritschel
COMMENTS:       11 pages, 12 figures
HIGHLIGHT:      We suggest representing light field (LF) videos as "one-off" neural networks (NN), i.e., a learned mapping from view-plus-time coordinates to high-resolution color values, trained on sparse views.

48, TITLE:      DIET: Lightweight Language Understanding for Dialogue Systems
http://arxiv.org/abs/2004.09936
AUTHORS:        Tanja Bunk ; Daksh Varshneya ; Vladimir Vlasov ; Alan Nichol
COMMENTS:       v2: Fix a typo in table 5
HIGHLIGHT:      We introduce the Dual Intent and Entity Transformer (DIET) architecture, and study the effectiveness of different pre-trained representations on intent and entity prediction, two common dialogue language understanding tasks.

49, TITLE:      Deep Generation of Coq Lemma Names Using Elaborated Terms
http://arxiv.org/abs/2004.07761
AUTHORS:        Pengyu Nie ; Karl Palmskog ; Junyi Jessy Li ; Milos Gligoric
COMMENTS:       Accepted in International Joint Conference on Automated Reasoning (IJCAR 2020). With Appendix
HIGHLIGHT:      We present novel generation models for learning and suggesting lemma names for Coq projects.

50, TITLE:      Social Bias Frames: Reasoning about Social and Power Implications of Language
http://arxiv.org/abs/1911.03891
AUTHORS:        Maarten Sap ; Saadia Gabriel ; Lianhui Qin ; Dan Jurafsky ; Noah A. Smith ; Yejin Choi
COMMENTS:       ACL 2020 Camera Ready; Data available at http://tinyurl.com/social-bias-frames
HIGHLIGHT:      We introduce Social Bias Frames, a new conceptual formalism that aims to model the pragmatic frames in which people project social biases and stereotypes onto others. In addition, we introduce the Social Bias Inference Corpus to support large-scale modelling and evaluation with 150k structured annotations of social media posts, covering over 34k implications about a thousand demographic groups.

51, TITLE:      Meta-Embeddings Based On Self-Attention
http://arxiv.org/abs/2003.01371
AUTHORS:        Qichen Li ; Xiaoke Jiang ; Jun Xia ; Jian Li
HIGHLIGHT:      In this paper, we devise a new meta-embedding model based on the self-attention mechanism, namely the Duo.

52, TITLE:      Generative Adversarial Networks are Special Cases of Artificial Curiosity (1990) and also Closely Related to Predictability Minimization (1991)
http://arxiv.org/abs/1906.04493
AUTHORS:        Juergen Schmidhuber
COMMENTS:       15 pages, 1 figure, 104 references
HIGHLIGHT:      I review unsupervised or self-supervised neural networks playing minimax games in game-theoretic settings: (i) Artificial Curiosity (AC, 1990) is based on two such networks.

53, TITLE:      LaserFlow: Efficient and Probabilistic Object Detection and Motion Forecasting
http://arxiv.org/abs/2003.05982
AUTHORS:        Gregory P. Meyer ; Jake Charland ; Shreyash Pandey ; Ankit Laddha ; Carlos Vallespi-Gonzalez ; Carl K. Wellington
HIGHLIGHT:      In this work, we present LaserFlow, an efficient method for 3D object detection and motion forecasting from LiDAR.

54, TITLE:      Renegotiation and recursion in Bitcoin contracts
http://arxiv.org/abs/2003.00296
AUTHORS:        Massimo Bartoletti ; Maurizio Murgia ; Roberto Zunino
COMMENTS:       Full version of the paper presented at COORDINATION 2020
HIGHLIGHT:      In this paper, we extend BitML with a new primitive for contract renegotiation.

55, TITLE:      A Machine Learning Approach to Persian Text Readability Assessment Using a Crowdsourced Dataset
http://arxiv.org/abs/1810.06639
AUTHORS:        Hamid Mohammadi ; Seyed Hossein Khasteh
COMMENTS:       15 pages, 4 figures, 4 tables, 7 equations
HIGHLIGHT:      In the present research, the first Persian dataset for text readability assessment was gathered and the first model for Persian text readability assessment using machine learning was introduced.

56, TITLE:      Adversarial Distortion for Learned Video Compression
http://arxiv.org/abs/2004.09508
AUTHORS:        Vijay Veerabadran ; Reza Pourreza ; Amirhossein Habibian ; Taco Cohen
HIGHLIGHT:      In this paper, we present a novel adversarial lossy video compression model.

57, TITLE:      G-VAE: A Continuously Variable Rate Deep Image Compression Framework
http://arxiv.org/abs/2003.02012
AUTHORS:        Ze Cui ; Jing Wang ; Bo Bai ; Tiansheng Guo ; Yihui Feng
HIGHLIGHT:      In this paper, we propose a novel image compression framework G-VAE (Gained Variational Autoencoder), which could achieve continuously variable rate in a single model.

58, TITLE:      Effective Reinforcement Learning through Evolutionary Surrogate-Assisted Prescription
http://arxiv.org/abs/2002.05368
AUTHORS:        Olivier Francon ; Santiago Gonzalez ; Babak Hodjat ; Elliot Meyerson ; Risto Miikkulainen ; Xin Qiu ; Hormoz Shahrzad
HIGHLIGHT:      This paper introduces a general such approach, called Evolutionary Surrogate-Assisted Prescription, or ESP.

59, TITLE:      Feedback Linearization for Unknown Systems via Reinforcement Learning
http://arxiv.org/abs/1910.13272
AUTHORS:        Tyler Westenbroek ; David Fridovich-Keil ; Eric Mazumdar ; Shreyas Arora ; Valmik Prabhu ; S. Shankar Sastry ; Claire J. Tomlin
HIGHLIGHT:      We present a novel approach to control design for nonlinear systems which leverages model-free policy optimization techniques to learn a linearizing controller for a physical plant with unknown dynamics.