xtcheng666

xtcheng666's Stars

• amusi/CVPR2024-Papers-with-Code

  CVPR 2024 论文和开源项目合集

  18.7k2912032.6k

• pytorch/vision

  Datasets, Transforms and Models specific to Computer Vision

  Language:Python16.4k4473.3k7k

• utkuozbulak/pytorch-cnn-visualizations

  Pytorch implementation of convolutional neural network visualization techniques

  Language:Python7.9k1151061.5k

• facebookresearch/dino

  PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO

  Language:Python6.5k67247924

• pytorch/captum

  Model interpretability and understanding for PyTorch

  Language:Python5k279553505

• sail-sg/EditAnything

  Edit anything in images powered by segment-anything, ControlNet, StableDiffusion, etc. (ACM MM)

  Language:Python3.3k4057195

• jeonsworld/ViT-pytorch

  Pytorch reimplementation of the Vision Transformer (An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale)

  Language:Jupyter Notebook2k1355377

• cdpierse/transformers-interpret

  Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.

  Language:Jupyter Notebook1.3k217898

• jacobgil/vit-explain

  Explainability for Vision Transformers

  Language:Python885626102

• understandable-machine-intelligence-lab/Quantus

  Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations

  Language:Jupyter Notebook5691013375

• yangsenius/TransPose

  PyTorch Implementation for "TransPose: Keypoint localization via Transformer", ICCV 2021.

  Language:Jupyter Notebook364124960

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

  Recent weakly supervised semantic segmentation paper

  28311422

• PaddlePaddle/InterpretDL

  InterpretDL: Interpretation of Deep Learning Models，基于『飞桨』的模型可解释性算法库。

  Language:Python241114338

• samiraabnar/attention_flow

  Language:Jupyter Notebook22781131

• HiLab-git/WSL4MIS

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

  Language:Python21751834

• yaoyz96/els-cas-templates

  Elsevier template 'els-cas-templates'.

  Language:TeX1713419

• yaoyz96/elsarticle

  Elsevier template 'elsarticle'.

  Language:TeX1373121

• ChuHan89/WSSS-Tissue

  Language:Python604119

• PengtaoJiang/OAA

  Code for our ICCV 2019 paper "Integral Object Mining via Online Attention Accumulation"

  Language:C++514108

• xmed-lab/OEEM

  MICCAI 2022: Online Easy Example Mining for Weakly-supervised Gland Segmentation from Histology Images

  Language:Jupyter Notebook35145

• zhaozhengChen/LPCAM

  The official code of CVPR 2023 paper (Extracting Class Activation Maps from Non-Discriminative Features as well).

  Language:Python31194

• AngelosNal/Vision-DiffMask

  Official PyTorch implementation of Vision DiffMask, a post-hoc interpretation method for vision models.

  Language:Jupyter Notebook29413

• windstormer/AME-CAM

  Language:Python23101

• vaynexie/CausalX-ViT

  Language:Jupyter Notebook16230

• Vison307/PistoSeg

  Code Repository for AAAI23 paper "Weakly-Supervised Semantic Segmentation for Histopathology Images Based on Dataset Synthesis and Feature Consistency Constraint"

  Language:Jupyter Notebook12371

• HiLab-git/HAMIL

  Language:Python11140

• iia-iccv23/iia

  The official PyTorch implementation of the Iterated Integrated Attributions (IIA) method.

  Language:Python9103

• tangli-udel/DRE

  The Pytorch implementation for "Are Data-driven Explanations Robust against Out-of-distribution Data?" (CVPR 2023)

  Language:Python9212

• jiaminchen-1031/transformerinterp

  Pytorch Implementation for [TMLR] "Beyond Intuition: Rethinking Token Attributions inside Transformers"

  Language:Python7

• piotr-komorowski/towards-evaluating-explanations-of-vit

  [XAI4CV CVPR 2023] Towards Evaluating Explanations of Vision Transformers for Medical Imaging

  Language:Python70