Stars
A Collection of Papers and Codes in ECCV2022 about low level vision
A Collection of Papers and Codes in NeurIPS2022/2021 related to Low-Level Vision
A Collection of Papers and Codes in AAAI2022 related to Low-Level Vision
A Collection of Papers and Codes in ICCV2023/2021 about low level vision
A Collection of Papers and Codes in CVPR2023/2022 about low level vision
Face alignment with similarity transform based on MTCNN and RetinaFace.
official pytorch implementation of "Deep Metric Learning with Spherical Embedding", NeurIPS 2020
ICCV2021, Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet
Official benchmark code for the CVPR 2021 paper "How Well Do Self-Supervised Models Transfer?"
A PyTorch-based library for semi-supervised learning (NeurIPS'21)
Codebase for Image Classification Research, written in PyTorch.
A state-of-the-art semi-supervised method for image recognition
The official repo of the CVPR2021 oral paper: Representative Batch Normalization with Feature Calibration
Official Code of AAAI 2021 Paper "Multi-level Distance Regularization for Deep Metric Learning"
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
XBM: Cross-Batch Memory for Embedding Learning
Official PyTorch Implementation of ProxyGML Loss for Deep Metric Learning, NeurIPS 2020 (spotlight)
DomainBed is a suite to test domain generalization algorithms
Corruption and Perturbation Robustness (ICLR 2019)
Distributionally robust neural networks for group shifts
(ECCV 2020) This repo contains code for "DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning" (https://arxiv.org/abs/2004.13458), which extends vanilla DML with auxiliary and self-sup…
State-of-the-art 2D and 3D Face Analysis Project
Command-line program to download videos from YouTube.com and other video sites
PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations
SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners