Argilla is a collaboration tool for AI engineers and domain experts to build high-quality datasets
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Updated
Sep 18, 2024 - Python
Argilla is a collaboration tool for AI engineers and domain experts to build high-quality datasets
Code for ALBEF: a new vision-language pre-training method
Data-efficient and weakly supervised computational pathology on whole slide images - Nature Biomedical Engineering
A curated (most recent) list of resources for Learning with Noisy Labels
Hierarchical Image Pyramid Transformer - CVPR 2022 (Oral)
Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation [Inoue+, CVPR2018].
Single-Stage Semantic Segmentation from Image Labels (CVPR 2020)
Weakly-supervised object detection.
Mask-Free Video Instance Segmentation [CVPR 2023]
DSMIL: Dual-stream multiple instance learning networks for tumor detection in Whole Slide Image
Weakly Supervised Instance Segmentation using Class Peak Response, in CVPR 2018 (Spotlight)
Weakly Supervised Learning for Findings Detection in Medical Images
[EMNLP 2020] Text Classification Using Label Names Only: A Language Model Self-Training Approach
BOND: BERT-Assisted Open-Domain Name Entity Recognition with Distant Supervision
PyTorch implementation of "WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation", CVPR 2017
Recent weakly supervised semantic segmentation paper
Weakly Supervised Segmentation with Tensorflow. Implements instance segmentation as described in Simple Does It: Weakly Supervised Instance and Semantic Segmentation, by Khoreva et al. (CVPR 2017).
Caffe codes for our papers "Multiple Instance Detection Network with Online Instance Classifier Refinement" and "PCL: Proposal Cluster Learning for Weakly Supervised Object Detection".
[NeurIPS 2021] WRENCH: Weak supeRvision bENCHmark
[NAACL 2021] This is the code for our paper `Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach'.
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