Transformer总结
Contents
Attention
- Recurrent Models of Visual Attention [2014 deepmind NIPS]
- Neural Machine Translation by Jointly Learning to Align and Translate [ICLR 2015]
OverallSurvey
- Efficient Transformers: A Survey [paper]
- A Survey on Visual Transformer [paper]
- Transformers in Vision: A Survey [paper]
NLP
Language
- Sequence to Sequence Learning with Neural Networks [NIPS 2014] [paper] [code]
- End-To-End Memory Networks [NIPS 2015] [paper] [code]
- Attention is all you need [NIPS 2017] [paper] [code]
- Bidirectional Encoder Representations from Transformers: BERT [paper] [code] [pretrained-models]
- Reformer: The Efficient Transformer [ICLR2020] [paper] [code]
- Linformer: Self-Attention with Linear Complexity [AAAI2020] [paper] [code]
- GPT-3: Language Models are Few-Shot Learners [NIPS 2020] [paper] [code]
Speech
- Dual-Path Transformer Network: Direct Context-Aware Modeling for End-to-End Monaural Speech Separation [INTERSPEECH 2020] [paper] [code]
CV
Backbone_Classification
Papers and Codes
- CoaT: Co-Scale Conv-Attentional Image Transformers [arxiv 2021] [paper] [code]
- SiT: Self-supervised vIsion Transformer [arxiv 2021] [paper] [code]
- VIT: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale [VIT] [ICLR 2021] [paper] [code]
- Trained with extra private data: do not generalized well when trained on insufficient amounts of data
- DeiT: Data-efficient Image Transformers [arxiv2021] [paper] [code]
- Token-based strategy and build upon VIT and convolutional models
- Transformer in Transformer [arxiv 2021] [paper] [code1] [code-official]
- OmniNet: Omnidirectional Representations from Transformers [arxiv2021] [paper]
- Gaussian Context Transformer [CVPR 2021] [paper]
- General Multi-Label Image Classification With Transformers [CVPR 2021] [paper] [code]
- Scaling Local Self-Attention for Parameter Efficient Visual Backbones [CVPR 2021] [paper]
- T2T-ViT: Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet [ICCV 2021] [paper] [code]
- Swin Transformer: Hierarchical Vision Transformer using Shifted Windows [ICCV 2021] [paper] [code]
- Bias Loss for Mobile Neural Networks [ICCV 2021] [paper] [[code()]]
- Vision Transformer with Progressive Sampling [ICCV 2021] [paper] [[code(https://github.com/yuexy/PS-ViT)]]
- Rethinking Spatial Dimensions of Vision Transformers [ICCV 2021] [paper] [code]
- Rethinking and Improving Relative Position Encoding for Vision Transformer [ICCV 2021] [paper] [code]
Interesting Repos
- Convolutional Cifar10
- vision-transformers-cifar10
- Found that performance was worse than simple resnet18
- The influence of hyper-parameters: dim of vit, etc.
- ViT-pytorch
- Using pretrained weights can get better results
Self-Supervised
- Emerging Properties in Self-Supervised Vision Transformers [ICCV 2021] [paper] [code]
- An Empirical Study of Training Self-Supervised Vision Transformers [ICCV 2021] [paper] [code]
Interpretability and Robustness
- Transformer Interpretability Beyond Attention Visualization [CVPR 2021] [paper] [code]
- On the Adversarial Robustness of Visual Transformers [arxiv 2021] [paper]
- Robustness Verification for Transformers [ICLR 2020] [paper] [code]
- Pretrained Transformers Improve Out-of-Distribution Robustness [ACL 2020] [paper] [code]
Detection
- DETR: End-to-End Object Detection with Transformers [ECCV2020] [paper] [code]
- Deformable DETR: Deformable Transformers for End-to-End Object Detection [ICLR2021] [paper] [code]
- End-to-End Object Detection with Adaptive Clustering Transformer [arxiv2020] [paper]
- UP-DETR: Unsupervised Pre-training for Object Detection with Transformers [[arxiv2020] [paper]
- Rethinking Transformer-based Set Prediction for Object Detection [arxiv2020] [paper] [zhihu]
- End-to-end Lane Shape Prediction with Transformers [WACV 2021] [paper] [code]
- ViT-FRCNN: Toward Transformer-Based Object Detection [arxiv2020] [paper]
- Line Segment Detection Using Transformers [CVPR 2021] [paper] [code]
- Facial Action Unit Detection With Transformers [CVPR 2021] [paper] [code]
- Adaptive Image Transformer for One-Shot Object Detection [CVPR 2021] [paper] [code]
- Self-attention based Text Knowledge Mining for Text Detection [CVPR 2021] [paper] [code]
- Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions [ICCV 2021] [paper] [code]
- Group-Free 3D Object Detection via Transformers [ICCV 2021] [paper] [code]
- Fast Convergence of DETR with Spatially Modulated Co-Attention [ICCV 2021] [paper] [code]
HOI
- End-to-End Human Object Interaction Detection with HOI Transformer [CVPR 2021] [paper] [code]
- HOTR: End-to-End Human-Object Interaction Detection with Transformers [CVPR 2021] [paper] [code]
Tracking
- Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking [CVPR 2021] [paper] [code]
- TransTrack: Multiple-Object Tracking with Transformer [CVPR 2021] [paper] [code]
- Transformer Tracking [CVPR 2021] [paper] [code]
- Learning Spatio-Temporal Transformer for Visual Tracking [ICCV 2021] [paper] [code]
Segmentation
- SETR : Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers [CVPR 2021] [paper] [code]
- Trans2Seg: Transparent Object Segmentation with Transformer [arxiv2021] [paper] [code]
- End-to-End Video Instance Segmentation with Transformers [arxiv2020] [paper] [zhihu]
- MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers [CVPR 2021] [paper] [official-code] [unofficial-code]
- Medical Transformer: Gated Axial-Attention for Medical Image Segmentation [arxiv 2020] [paper] [code]
- SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation [CVPR 2021] [paper] [code]
Reid
- Diverse Part Discovery: Occluded Person Re-Identification With Part-Aware Transformer [CVPR 2021] [paper] [code]
Localization
- LoFTR: Detector-Free Local Feature Matching with Transformers [CVPR 2021] [paper] [code]
- MIST: Multiple Instance Spatial Transformer [CVPR 2021] [paper] [code]
Generation
- Variational Transformer Networks for Layout Generation [CVPR 2021] [paper] [code]
- TransGAN: Two Transformers Can Make One Strong GAN [paper] [code]
- Taming Transformers for High-Resolution Image Synthesis [CVPR 2021] [paper] [code]
- iGPT: Generative Pretraining from Pixels [ICML 2020] [paper] [code]
- Generative Adversarial Transformers [arxiv 2021] [paper] [code]
- LayoutTransformer: Scene Layout Generation With Conceptual and Spatial Diversity [CVPR2021] [paper[https://openaccess.thecvf.com/content/CVPR2021/html/Yang_LayoutTransformer_Scene_Layout_Generation_With_Conceptual_and_Spatial_Diversity_CVPR_2021_paper.html]] [code]
- Spatial-Temporal Transformer for Dynamic Scene Graph Generation [ICCV 2021] [paper]
Inpainting
- STTN: Learning Joint Spatial-Temporal Transformations for Video Inpainting [ECCV 2020] [paper] [code]
Image enhancement
- Pre-Trained Image Processing Transformer [CVPR 2021] [paper]
- TTSR: Learning Texture Transformer Network for Image Super-Resolution [CVPR2020] [paper] [code]
Pose Estimation
- Pose Recognition with Cascade Transformers [CVPR 2021] [paper] [code]
- TransPose: Towards Explainable Human Pose Estimation by Transformer [arxiv 2020] [paper] [code]
- Hand-Transformer: Non-Autoregressive Structured Modeling for 3D Hand Pose Estimation [ECCV 2020] [paper]
- HOT-Net: Non-Autoregressive Transformer for 3D Hand-Object Pose Estimation [ACMMM 2020] [paper]
- End-to-End Human Pose and Mesh Reconstruction with Transformers [CVPR 2021] [paper] [code]
- 3D Human Pose Estimation with Spatial and Temporal Transformers [arxiv 2020] [paper] [code]
- End-to-End Trainable Multi-Instance Pose Estimation with Transformers [arxiv 2020] [paper]
Face
- Robust Facial Expression Recognition with Convolutional Visual Transformers [arxiv 2020] [paper]
- Clusformer: A Transformer Based Clustering Approach to Unsupervised Large-Scale Face and Visual Landmark Recognition [CVPR 2021] [paper] [code]
Video Understanding
- Is Space-Time Attention All You Need for Video Understanding? [arxiv 2020] [paper] [code]
- Temporal-Relational CrossTransformers for Few-Shot Action Recognition [CVPR 2021] [paper] [code]
- Self-Supervised Video Hashing via Bidirectional Transformers [CVPR 2021] [paper]
- SSAN: Separable Self-Attention Network for Video Representation Learning [CVPR 2021] [paper]
Depth-Estimation
Prediction
- Multimodal Motion Prediction with Stacked Transformers [CVPR 2021] [paper] [code]
- Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case [paper]
- Transformer networks for trajectory forecasting [ICPR 2020] [paper] [code]
- Spatial-Channel Transformer Network for Trajectory Prediction on the Traffic Scenes [arxiv 2021] [paper] [code]
- Pedestrian Trajectory Prediction using Context-Augmented Transformer Networks [ICRA 2020] [paper] [code]
- Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction [ECCV 2020] [paper] [code]
- Hierarchical Multi-Scale Gaussian Transformer for Stock Movement Prediction [paper]
- Single-Shot Motion Completion with Transformer [arxiv2021] [paper] [code]
NAS
- HR-NAS: Searching Efficient High-Resolution Neural Architectures with Transformers [CVPR 2021] [paper] [code]
- AutoFormer: Searching Transformers for Visual Recognition [ICCV 2021] [paper] [[code(https://github.com/microsoft/AutoML)]]
PointCloud
- Multi-Modal Fusion Transformer for End-to-End Autonomous Driving [CVPR 2021] [paper] [code]
- Point 4D Transformer Networks for Spatio-Temporal Modeling in Point Cloud Videos [CVPR 2021] [paper]
Fashion
Medical
- Lesion-Aware Transformers for Diabetic Retinopathy Grading [CVPR 2021] [paper]
Cross-Modal
- Thinking Fast and Slow: Efficient Text-to-Visual Retrieval with Transformers [CVPR 2021] [paper]
- Revamping Cross-Modal Recipe Retrieval with Hierarchical Transformers and Self-supervised Learning [CVPR2021] [paper] [code]
- Topological Planning With Transformers for Vision-and-Language Navigation [CVPR 2021] [paper]
- Multi-Stage Aggregated Transformer Network for Temporal Language Localization in Videos [CVPRR 2021] [paper]
- VLN BERT: A Recurrent Vision-and-Language BERT for Navigation [CVPR 2021] [paper] [code]
- Less Is More: ClipBERT for Video-and-Language Learning via Sparse Sampling [CVPR 2021] [paper] [code]
Reference
- Attention 机制详解1,2 zhihu1 zhihu2
- 自然语言处理中的自注意力机制(Self-attention Mechanism)
- Transformer模型原理详解 [zhihu] [csdn]
- 完全解析RNN, Seq2Seq, Attention注意力机制
- Seq2Seq and transformer implementation
- End-To-End Memory Networks [zhihu]
- Illustrating the key,query,value in attention
- Transformer in CV
- CVPR2021-Papers-with-Code
- ICCV2021-Papers-with-Code
Talk is cheap. Show me the code