weakly-supervised-learning 开源项目推荐（365 个）

argilla-io /argilla

Argilla 是一款协作工具，供 AI 工程师和领域专家构建高质量数据集

mahmoodlab /CLAM

计算病理学开源工具- Nature BME

salesforce /ALBEF

Code for ALBEF: a new vision-language pre-training method

weijiaheng /Advances-in-Label-Noise-Learning

A curated (most recent) list of resources for Learning with Noisy Labels

mahmoodlab /HIPT

Hierarchical Image Pyramid Transformer - CVPR 2022 (Oral)

binli123 /dsmil-wsi

DSMIL: Dual-stream multiple instance learning networks for tumor detection in Whole Slide Image

naoto0804 /cross-domain-detection

Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation [Inoue+, CVPR2018].

visinf /1-stage-wseg

Single-Stage Semantic Segmentation from Image Labels (CVPR 2020)

PengtaoJiang /Awesome-Weakly-Supervised-Semantic-Segmentation-Papers

Recent weakly supervised semantic segmentation paper

SysCV /MaskFreeVIS

Mask-Free Video Instance Segmentation [CVPR 2023]

NVlabs /wetectron

Weakly-supervised object detection.

ZhouYanzhao /PRM

Weakly Supervised Instance Segmentation using Class Peak Response, in CVPR 2018 (Spotlight)

thtang /CheXNet-with-localization

Weakly Supervised Learning for Findings Detection in Medical Images

yumeng5 /LOTClass

[EMNLP 2020] Text Classification Using Label Names Only: A Language Model Self-Training Approach

cliang1453 /BOND

BOND: BERT-Assisted Open-Domain Name Entity Recognition with Distant Supervision

durandtibo /wildcat.pytorch

PyTorch 实现了《WILDCAT：深度对集网的弱监督学习用于图像分类、逐点定位与分割》，CVPR 2017

HiLab-git /WSL4MIS

Scribbles or Points-based weakly-supervised learning for medical image segmentation, a strong baseline, and tutorial for research and application.

JieyuZ2 /wrench

[NeurIPS 2021] WRENCH: Weak supeRvision bENCHmark

philferriere /tfwss

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).

antoyang /VidChapters

[NeurIPS 2023 D&B] VidChapters-7M: Video Chapters at Scale