XLearning Group's repositories
2022-CVPR-AirNet
PyTorch implementation for All-In-One Image Restoration for Unknown Corruption (AirNet) (CVPR 2022)
2021-CVPR-Completer
PyTorch implementation for COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction (CVPR 2021)
2024-ICLR-Norton
Multi-granularity Correspondence Learning from Long-term Noisy Videos [ICLR 2024, Oral]
2021-AAAI-CC
Code for the paper "Contrastive Clustering" (AAAI 2021)
2021-IJCV-YOLY
PyTorch implementation for You Only Look Yourself: Unsupervised and Untrained Single Image Dehazing Neural Network (YOLY) (IJCV 2021)
2021-CVPR-MvCLN
PyTorch implementation for Partially View-aligned Representation Learning with Noise-robust Contrastive Loss (CVPR 2021)
2022-TPAMI-DCP
PyTorch implementation for Dual Contrastive Prediction for Incomplete Multi-view Representation Learning (TPAMI'22)
2023-CVPR-CODE
PyTorch implementation for Comprehensive and Delicate: An Efficient Transformer for Image Restoration (CVPR 2023).
2019-ICML-COMIC
COMIC: Multi-view Clustering Without Parameter Selection, International Conference on Machine Learning (ICML’19)
2022-CVPR-DART
PyTorch implementation for Learning with Twin Noisy Labels for Visible-Infrared Person Re-Identification (CVPR 2022).
2022-TPAMI-SURE
PyTorch implementation for Robust Multi-view Clustering with Incomplete Information (TPAMI 2022).
2023-ICCV-COMMON
This repo contains the code and data of "Graph Matching with Bi-level Noisy Correspondence".
Awesome-All-In-One-Image-Restoration
This is a summary of research on All-In-One Image/Video Restoration. There may be omissions. If anything is missing please get in touch with us. Our emails: liboyun.gm@gmail.com; gouyuanbiao@gmail.com; haiyuzhao.gm@gmail.com; wangwenxin.gm@gmail.com
2023-CVPR-FCMI
PyTorch implementation for Deep Fair Clustering via Maximizing and Minimizing Mutual Information: Theory, Algorithm and Metric (CVPR 2023).
2024-ICLR-READ
Pytorch implementation of "Test-time Adaption against Multi-modal Reliability Bias".
Awesome-Noisy-Correspondence
This is a summary of research on noisy correspondence. There may be omissions. If anything is missing please get in touch with us. Our emails: linyijie.gm@gmail.com yangmouxing@gmail.com qinyang.gm@gmail.com
2024-TIP-CREAM
PyTorch implementation for Cross-modal Retrieval with Noisy Correspondence via Consistency Refining and Mining (TIP 2024)
2022-NeurIPS-MSANet
Multi-Scale Adaptive Network for Single Image Denoising (NeurIPS 2022)
2024-AAAI-DIVIDE
Official implementation of "Decoupled Contrastive Multi-View Clustering with High-Order Random Walks", [AAAI 2024].
2023-IJCAI-ProImp
PyTorch implementation for Incomplete Multi-view Clustering via Prototype-based Imputation (IJCAI 2023)
2024-ICML-TAC
Code for the paper "Image Clustering with External Guidance" (ICML 2024)
2023-TPAMI-SMILE
PyTorch implementation for Semantic Invariant Multi-view Clustering with Fully Incomplete Information (SMILE), TPAMI 2023.