vision_pretraining.md

Visual Representation Learning

Visual Representation Learning on Image

• [2018 ArXiv] Representation Learning with Contrastive Predictive Coding, [paper], [bibtex], sources: [davidtellez/contrastive-predictive-coding], [flrngel/cpc-tensorflow], [jefflai108/Contrastive-Predictive-Coding-PyTorch].
• [2018 CVPR] Unsupervised Feature Learning via Non-Parametric Instance Discrimination, [paper], [bibtex], sources: [zhirongw/lemniscate.pytorch].
• [2019 CVPR] Revisiting Self-Supervised Visual Representation Learning, [paper], [bibtex], sources: [google/revisiting-self-supervised].
• [2019 ICCV] CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features, [paper], [bibtex], sources: [clovaai/CutMix-PyTorch].
• [2020 CVPR] Momentum Contrast for Unsupervised Visual Representation Learning, [paper], [bibtex], sources: [facebookresearch/moco].
• [2020 CVPR] Visual Commonsense R-CNN, [paper], [bibtex], sources: [Wangt-CN/VC-R-CNN].
• [2020 ICML] Generative Pretraining from Pixels, [paper], [bibtex], sources: [openai/image-gpt].
• [2020 NeurIPS] Big Self-Supervised Models are Strong Semi-Supervised Learners, [paper], [bibtex], sources: [google-research/simclr].
• [2020 NeurIPS] Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning, [paper], [bibtex], sources: [lucidrains/byol-pytorch].
• [2020 NeurIPS] Unsupervised Learning of Visual Features by Contrasting Cluster Assignments, [paper], [bibtex], sources: [facebookresearch/swav].
• [2020 ECCV] Big Transfer (BiT): General Visual Representation Learning, [paper], [bibtex], sources: [google-research/big_transfer].
• [2020 ICML] A Simple Framework for Contrastive Learning of Visual Representations, [paper], [bibtex], sources: [google-research/simclr].
• [2021 ICLR] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, [paper], [bibtex], sources: [google-research/vision_transformer].
• [2021 ICLR] Representation Learning via Invariant Causal Mechanisms, [paper], [bibtex].
• [2021 ArXiv] Conditional Positional Encodings for Vision Transformers, [paper], [bibtex], sources: [Meituan-AutoML/CPVT].
• [2021 ICML] DeiT: Training Data-Efficient Image Transformers & Distillation Through Attention, [paper], [bibtex], [supplementary], sources: [facebookresearch/deit].
• [2021 ICML] Understanding Self-Supervised Learning Dynamics without Contrastive Pairs, [paper], [bibtex], [slides], sources: [facebookresearch/luckmatters/ssl].
• [2021 ArXiv] Proactive Pseudo-Intervention: Contrastive Learning For Interpretable Vision Models, [paper], [bibtex].
• [2021 ArXiv] Adversarial Visual Robustness by Causal Intervention, [paper], [bibtex], sources: [KaihuaTang/Adversarial-Robustness-by-Causal-Intervention.pytorch].
• [2021 CVPR] Exploring Simple Siamese Representation Learning, [paper], [bibtex], sources: [facebookresearch/simsiam].
• [2021 CVPR] Pre-Trained Image Processing Transformer, [paper], [bibtex], [supplementary], sources: [huawei-noah/Pretrained-IPT].
• [2021 NeurIPS] DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification, [paper], [bibtex], [supplementary], sources: [raoyongming/DynamicViT].
• [2021 NeurIPS] (LV-ViT) All Tokens Matter: Token Labeling for Training Better Vision Transformers, [paper], [bibtex], sources: [zihangJiang/TokenLabeling].
• [2021 NeurIPS] Pay Attention to MLPs, [paper], [bibtex], sources: [rwightman/pytorch-image-models], [labmlai/annotated_deep_learning_paper_implementations], [xmu-xiaoma666/External-Attention-pytorch], [PaddleViT/gMLP].
• [2021 ICCV] Emerging Properties in Self-Supervised Vision Transformers, [paper], [bibtex], sources: [facebookresearch/dino].
• [2021 ICCV] Swin Transformer - Hierarchical Vision Transformer using Shifted Windows, [paper], [bibtex], sources: [microsoft/Swin-Transformer].
• [2021 ArXiv] Masked Autoencoders Are Scalable Vision Learners, [paper], [bibtex], sources: [facebookresearch/mae], [pengzhiliang/MAE-pytorch].
• [2022 ICLR] BEiT: BERT Pre-Training of Image Transformers, [paper], [bibtex], sources: [microsoft/beit], [huggingface/transformers/beit].
• [2022 ICLR] SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption, [paper], [bibtex].
• [2022 ArXiv] Patches are All You Need, [paper], [bibtex], sources: [locuslab/convmixer].
• [2022 CVPR] An Image Patch is a Wave - Quantum Inspired Vision MLP, [paper], [bibtex], sources: [huawei-noah/wavemlp_pytorch].

Visual Representation Learning on Video

• [2019 ICCV] Video Representation Learning by Dense Predictive Coding, [paper], [bibtex], sources: [TengdaHan/DPC].
• [2021 ArXiv] TCLR: Temporal Contrastive Learning for Video Representation, [paper], [bibtex].
• [2021 ArXiv] Self-supervised Video Retrieval Transformer Network, [paper], [bibtex].
• [2021 ArXiv] Video Swin Transformer, [paper], [bibtex], sources: [SwinTransformer/Video-Swin-Transformer].
• [2021 ICCV] ViViT: A Video Vision Transformer, [paper], [bibtex], sources: [rishikksh20/ViViT-pytorch].
• [2021 CVPR] The Blessings of Unlabeled Background in Untrimmed Videos, [paper], [bibtex], sources: [liuyuancv/WTAL_blessing].
• [2021 CVPR] VideoMoCo: Contrastive Video Representation Learning with Temporally Adversarial Examples, [paper], [bibtex], sources: [tinapan-pt/VideoMoCo].
• [2022 CVPR] BEVT: BERT Pretraining of Video Transformers, [paper], [bibtex], sources: [xyzforever/BEVT].