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: [email protected] [email protected] qinyang.gm…

[NeurIPS 2021] Moment-DETR code and QVHighlights dataset

[NeurIPS 2022 Spotlight] VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training

Collect some papers about transformer with vision. Awesome Transformer with Computer Vision (CV)

Official PyTorch implementation of Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion (CVPR 2020)

PyTorch implementation of MAE https//arxiv.org/abs/2111.06377

Recent papers and codes related to deep learning/deep neural network based image compression and video coding framework.

Official code for our CVPR '22 paper "Dataset Distillation by Matching Training Trajectories"

UMT is a unified and flexible framework which can handle different input modality combinations, and output video moment retrieval and/or highlight detection results.

Implementation of Cross-category Video Highlight Detection via Set-based Learning (ICCV 2021).

Open-source code for paper "Dataset Distillation"

Official implementation for (Refine Myself by Teaching Myself : Feature Refinement via Self-Knowledge Distillation, CVPR-2021)

Code for paper: DivideMix: Learning with Noisy Labels as Semi-supervised Learning

AAAI 2021: Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

a distributed deep learning platform

A curated list of resources for Learning with Noisy Labels

整理AI相关领域的一些综述

Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval(CVPR2018)

Cross-Modal Retrieval, triplet loss, Pytorch, Resnet18, Bert, Deep Hashing

👾 Fast and simple video download library and CLI tool written in Go

A simple Python library and dataset for VQA

Tensorflow implementation of Multimodal Compact Bilinear Pooling for VQA

Inspired by "Show and Tell: A Neural Image Caption Generator"

A image caption model trained with Keras on selected MS COCO dataset

The implementation of "End-to-End Multi-Task Learning with Attention" [CVPR 2019].