Recent Transformer-based CV and related works. Welcome to comment/contribute!

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• Google PaLM: Scaling Language Modeling with Pathways, [Paper]

• OpenAI DALL·E 2 [Page], [Paper]

• SCENIC: A JAX Library for Computer Vision Research and Beyond, [Code]

• V-L joint learning study (with good tables): [METER], [Kaleido-BERT]

• Attention is all you need, [Paper]

• OpenAI CLIP [Page], [Paper], [Code], [arXiv]

• OpenAI DALL·E [Page], [Code], [Paper]

• huggingface/transformers

• Kyubyong/transformer, TF

• jadore801120/attention-is-all-you-need-pytorch, Torch

• krasserm/fairseq-image-captioning

• PyTorch Transformers Tutorials

• ictnlp/awesome-transformer

• basicv8vc/awesome-transformer

• dk-liang/Awesome-Visual-Transformer

• yuewang-cuhk/awesome-vision-language-pretraining-papers

• (arXiv 2022.03) A Roadmap for Big Model, [Paper]

• (arXiv 2022.03) Transformers Meet Visual Learning Understanding: A Comprehensive Review, [[Paper]](https://arxiv.org/pdf/2203.12944.pdf）

• (arXiv 2022.03) Recent Advances in Vision Transformer: A Survey and Outlook of Recent Work, [Paper], [Project]

• (arXiv 2022.02) A Survey of Vision-Language Pre-Trained Models, [Paper]

• (arXiv 2022.02) VLP: A Survey on Vision-Language Pre-training, [Paper]

• (arXiv 2022.02) Transformer for Graphs: An Overview from Architecture Perspective, [Paper]

• (arXiv 2022.01) Video Transformers: A Survey, [Paper]

• (arXiv 2021.11) ARE WE READY FOR A NEW PARADIGM SHIFT? A SURVEY ON VISUAL DEEP MLP, [Paper]

• (arXiv 2021.11) A Survey of Visual Transformers, [Paper]

• (arXiv 2021.09) Survey: Transformer based Video-Language Pre-training, [Paper]

• (arXiv 2021.06) A Survey of Transformers, [Paper]

• (arXiv 2021.06) Attention mechanisms and deep learning for machine vision: A survey of the state of the art, [Paper]

• (arXiv 2021.06) Pre-Trained Models: Past, Present and Future, [Paper]

• (arXiv 2021.05) Can Attention Enable MLPs To Catch Up With CNNs? [Paper]

• (arXiv 2021.03) A Practical Survey on Faster and Lighter Transformers, [Paper]

• (arXiv 2021.03) Perspectives and Prospects on Transformer Architecture for Cross-Modal Tasks with Language and Vision, [Paper]

• (arXiv 2021.01) A Survey on Visual Transformer, [Paper]

• (arXiv 2020.9) Efficient Transformers: A Survey, [Paper]

• (arXiv 2020.1) Transformers in Vision: A Survey, [Paper]

• (arXiv 2022.04) RELVIT: CONCEPT-GUIDED VISION TRANSFORMER FOR VISUAL RELATIONAL REASONING, [Paper]

• (arXiv 2022.04) Unsupervised Human Action Recognition with Skeletal Graph Laplacian and Self-Supervised Viewpoints Invariance, [Paper], [Code]

• (arXiv 2022.04) Learning Future Object Prediction with a Spatiotemporal Detection Transformer, [Paper]

• (arXiv 2022.04) R^2-Trans: Fine-Grained Visual Categorization with Redundancy Reduction, [Paper], [Code]

• (arXiv 2022.04) A New Dataset and Transformer for Stereoscopic Video Super-Resolution, [Paper], [Code]

• (arXiv 2022.04) Transformer-Guided Convolutional Neural Network for Cross-View Geolocalization, [Paper]

• (arXiv 2022.04) Multi-Scale Features and Parallel Transformers Based Image Quality Assessment, [Paper], [Code]

• (arXiv 2022.04) BTranspose: Bottleneck Transformers for Human Pose Estimation with Self-Supervised Pre-Training, [Paper]

• (arXiv 2022.04) Human-Object Interaction Detection via Disentangled Transformer, [Paper]

• (arXiv 2022.04) ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models, [Paper]

• (arXiv 2022.04) Interactiveness Field in Human-Object Interactions, [Paper], [Code]

• (arXiv 2022.04) DeiT III: Revenge of the ViT, [Paper]

• (arXiv 2022.04) Residual Swin Transformer Channel Attention Network for Image Demosaicing, [Paper]

• (arXiv 2022.04) Neighborhood Attention Transformer, [Paper], [Code]

• (arXiv 2022.04) MiniViT: Compressing Vision Transformers with Weight Multiplexing, [Paper], [Code]

• (arXiv 2022.04) ViTOL: Vision Transformer for Weakly Supervised Object Localization, [Paper], [Code]

• (arXiv 2022.04) What Matters in Language Conditioned Robotic Imitation Learning, [Paper], [Code]

• (arXiv 2022.04) Consistency driven Sequential Transformers Attention Model for Partially Observable Scenes, [Paper]

• (arXiv 2022.04) ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension, [Paper]

• (arXiv 2022.04) Are Multimodal Transformers Robust to Missing Modality? [Paper]

• (arXiv 2022.04) TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentation, [Paper], [Code]

• (arXiv 2022.04) X-DETR: A Versatile Architecture for Instance-wise Vision-Language Tasks, [Paper]

• (arXiv 2022.04) Event Transformer, [Paper]

• (arXiv 2022.04) Evaluating Vision Transformer Methods for Deep Reinforcement Learning from Pixels, [Paper]

• (arXiv 2022.04) ManiTrans: Entity-Level Text-Guided Image Manipulation via Token-wise Semantic Alignment and Generation, [Paper], [Code]

• (arXiv 2022.04) Multimodal Transformer for Nursing Activity Recognition, [Paper], [Code]

• (arXiv 2022.04) Robust Cross-Modal Representation Learning with Progressive Self-Distillation, [Paper]

• (arXiv 2022.04) Stripformer: Strip Transformer for Fast Image Deblurring, [Paper]

• (arXiv 2022.04) No Token Left Behind: Explainability-Aided Image Classification and Generation, [Paper]

• (arXiv 2022.04) Fashionformer: A Simple, Effective and Unified Baseline for Human Fashion Segmentation and Recognition, [Paper], [Code]

• (arXiv 2022.04) Panoptic-PartFormer: Learning a Unified Model for Panoptic Part Segmentation, [Paper], [Code]

• (arXiv 2022.04) DILEMMA: Self-Supervised Shape and Texture Learning with Transformers, [Paper]

• (arXiv 2022.04) Learning Trajectory-Aware Transformer for Video Super-Resolution, [Paper], [Code]

• (arXiv 2022.04) Learning to Induce Causal Structure, [Paper]

• (arXiv 2022.04) Consistency Learning via Decoding Path Augmentation for Transformers in Human Object Interaction Detection, [Paper], [Code]

• (arXiv 2022.04) Category-Aware Transformer Network for Better Human-Object Interaction Detection, [Paper]

• (arXiv 2022.04) Does Robustness on ImageNet Transfer to Downstream Tasks?, [Paper]

• (arXiv 2022.04) POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression Recognition, [Paper], [Code]

• (arXiv 2022.04) Vision Transformers for Single Image Dehazing, [Paper], [Code]

• (arXiv 2022.04) Underwater Image Enhancement Using Pre-trained Transformer, [Paper]

• (arXiv 2022.04) Event Transformer. A sparse-aware solution for efficient event data processing, [Paper], [Code]

• (arXiv 2022.04) PSTR: End-to-End One-Step Person Search With Transformers, [Paper], [Code]

• (arXiv 2022.04) Adapting CLIP For Phrase Localization Without Further Training, [Paper], [Code]

• (arXiv 2022.04) FineDiving: A Fine-grained Dataset for Procedure-aware Action Quality Assessment, [Paper], [Project]

• (arXiv 2022.04) DaViT: Dual Attention Vision Transformers, [Paper], [Code]

• (arXiv 2022.04) Unsupervised Prompt Learning for Vision-Language Models, [Paper], [Code]

• (arXiv 2022.04) Long Video Generation with Time-Agnostic VQGAN and Time-Sensitive Transformer, [Paper], [Project]

• (arXiv 2022.04) Unified Contrastive Learning in Image-Text-Label Space, [Paper], [Code]

• (arXiv 2022.04) HunYuan_tvr for Text-Video Retrivial, [Paper]

• (arXiv 2022.04) LEARNING TO COMPOSE SOFT PROMPTS FOR COMPOSITIONAL ZERO-SHOT LEARNING, [Paper]

• (arXiv 2022.04) End-to-End Zero-Shot HOI Detection via Vision and Language Knowledge Distillation, [Paper], [Code]

• (arXiv 2022.04) Temporal Alignment Networks for Long-term Video, [Paper], [Code]

• (arXiv 2022.04) Unleashing Vanilla Vision Transformer with Masked Image Modeling for Object Detection, [Paper], [Code]

• (arXiv 2022.04) MixFormer: Mixing Features across Windows and Dimensions, [Paper], [Code]

• (arXiv 2022.04) CM3: A CAUSAL MASKED MULTIMODAL MODEL OF THE INTERNET, [Paper]

• (arXiv 2022.04) DO AS I CAN, NOT AS I SAY: GROUNDING LANGUAGE IN ROBOTIC AFFORDANCES, [Paper], [Project]

• (arXiv 2022.04) TransGeo: Transformer Is All You Need for Cross-view Image Geo-localization, [Paper], [Code]

• (arXiv 2022.04) Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language, [Paper], [Project]

• (arXiv 2022.04) Vision Transformer with Cross-attention by Temporal Shift for Efficient Action Recognition, [Paper]

• (arXiv 2022.04) Learning Audio-Video Modalities from Image Captions, [Paper]

• (arXiv 2022.04) Improving Vision Transformers by Revisiting High-frequency Components, [Paper]

• (arXiv 2022.04) POS-BERT: Point Cloud One-Stage BERT Pre-Training, [Paper], [Code]

• (arXiv 2022.04) BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation, [Paper], [Code]

• (arXiv 2022.04) BatchFormerV2: Exploring Sample Relationships for Dense Representation Learning, [Paper]

• (arXiv 2022.04) TransRAC: Encoding Multi-scale Temporal Correlation with Transformers for Repetitive Action Counting, [Paper]

• (arXiv 2022.04) Long Movie Clip Classification with State-Space Video Models, [Paper], [Code]

• (arXiv 2022.04) TALLFormer: Temporal Action Localization with Long-memory Transformer, [Paper], [Code]

• (arXiv 2022.04) MultiMAE: Multi-modal Multi-task Masked Autoencoders, [Paper], [Project]

• (arXiv 2022.04) “This is my unicorn, Fluffy”: Personalizing frozen vision-language representations, [Paper]

• (arXiv 2022.04) SE(3)-Equivariant Attention Networks for Shape Reconstruction in Function Space, [Paper]

• (arXiv 2022.04) Multi-View Transformer for 3D Visual Grounding, [Paper], [Code]

• (arXiv 2022.04) VISION TRANSFORMER EQUIPPED WITH NEURAL RESIZER ON FACIAL EXPRESSION RECOGNITION TASK, [Paper]

• (arXiv 2022.04) Dual-AI: Dual-path Actor Interaction Learning for Group Activity Recognition, [Paper], [Project]

• (arXiv 2022.04) Detector-Free Weakly Supervised Group Activity Recognition, [Paper]

• (arXiv 2022.04) Joint Hand Motion and Interaction Hotspots Prediction from Egocentric Videos, [Paper], [Project]

• (arXiv 2022.04) What to look at and where: Semantic and Spatial Refined Transformer for detecting human-object interactions, [Paper]

• (arXiv 2022.04) MaxViT: Multi-Axis Vision Transformer, [Paper]

• (arXiv 2022.03) Spatial-Temporal Parallel Transformer for Arm-Hand Dynamic Estimation, [Paper]

• (arXiv 2022.03) ViSTA: Vision and Scene Text Aggregation for Cross-Modal Retrieval, [Paper]

• (arXiv 2022.03) ReSTR: Convolution-free Referring Image Segmentation Using Transformers, [Paper], [Project]

• (arXiv 2022.03) CREATE: A Benchmark for Chinese Short Video Retrieval and Title Generation, [Paper]

• (arXiv 2022.03) Deformable Video Transformer, [Paper]

• (arXiv 2022.03) End-to-End Trajectory Distribution Prediction Based on Occupancy Grid Maps, [Paper]

• (arXiv 2022.03) CRAFT: Cross-Attentional Flow Transformer for Robust Optical Flow, [Paper], [Code]

• (arXiv 2022.03) VL-InterpreT: An Interactive Visualization Tool for Interpreting Vision-Language Transformers, [Paper], [App]

• (arXiv 2022.03) TransEditor: Transformer-Based Dual-Space GAN for Highly Controllable Facial Editing, [Paper], [Code]

• (arXiv 2022.03) BEVFormer: Learning Bird’s-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers, [Paper], [Code]

• (arXiv 2022.03) Visual Prompting: Modifying Pixel Space to Adapt Pre-trained Models, [Paper], [Code]

• (arXiv 2022.03) Bringing Old Films Back to Life, [Paper], [Code]

• (arXiv 2022.03) Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model, [Paper], [Code]

• (arXiv 2022.03) SeqTR: A Simple yet Universal Network for Visual Grounding, [Paper], [Code]

• (arXiv 2022.03) InstaFormer: Instance-Aware Image-to-Image Translation with Transformer, [Paper]

• (arXiv 2022.03) Omni-DETR: Omni-Supervised Object Detection with Transformers, [Paper], [Code]

• (arXiv 2022.03) Learning Program Representations for Food Images and Cooking Recipes, [Paper], [Project]

• (arXiv 2022.03) ITTR: Unpaired Image-to-Image Translation with Transformers, [Paper]

• (arXiv 2022.03) VPTR: Efficient Transformers for Video Prediction, [Paper], [Code]

• (arXiv 2022.03) Parameter-efficient Fine-tuning for Vision Transformers, [Paper]

• (arXiv 2022.03) TubeDETR: Spatio-Temporal Video Grounding with Transformers, [Paper], [Code]

• (arXiv 2022.03) Exploring Plain Vision Transformer Backbones for Object Detection, [Paper]

• (arXiv 2022.03) PROMPTDET: EXPAND YOUR DETECTOR VOCABULARY WITH UNCURATED IMAGES, [Paper], [Code]

• (arXiv 2022.03) Few-Shot Object Detection with Fully Cross-Transformer, [Paper]

• (arXiv 2022.03) Unified Transformer Tracker for Object Tracking, [Paper]

• (arXiv 2022.03) X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval, [Paper], [Code]

• (arXiv 2022.03) Fine-tuning Image Transformers using Learnable Memory, [Paper]

• (arXiv 2022.03) MAT: Mask-Aware Transformer for Large Hole Image Inpainting, [Paper], [Code]

• (arXiv 2022.03) mc-BEiT: Multi-choice Discretization for Image BERT Pre-training, [Paper]

• (arXiv 2022.03) End-to-End Transformer Based Model for Image Captioning, [Paper]

• (arXiv 2022.03) Hybrid Routing Transformer for Zero-Shot Learning, [Paper]

• (arXiv 2022.03) TREATMENT LEARNING TRANSFORMER FOR NOISY IMAGE CLASSIFICATION, [Paper]

• (arXiv 2022.03) Do Vision-Language Pretrained Models Learn Primitive Concepts?, [Paper]

• (arXiv 2022.03) Transformer Inertial Poser: Attention-based Real-time Human Motion Reconstruction from Sparse IMUs, [Paper]

• (arXiv 2022.03) SepViT: Separable Vision Transformer, [Paper]

• (arXiv 2022.03) MatteFormer: Transformer-Based Image Matting via Prior-Tokens, [Paper], [Code]

• (arXiv 2022.03) Feature Selective Transformer for Semantic Image Segmentation, [Paper]

• (arXiv 2022.03) Bridge-Prompt: Towards Ordinal Action Understanding in Instructional Videos, [Paper], [Code]

• (arXiv 2022.03) RSTT: Real-time Spatial Temporal Transformer for Space-Time Video Super-Resolution, [Paper], [Code]

• (arXiv 2022.03) Single-Stream Multi-Level Alignment for Vision-Language Pretraining, [Paper]

• (arXiv 2022.03) Beyond Masking: Demystifying Token-Based Pre-Training for Vision Transformers, [Paper], [Code]

• (arXiv 2022.03) Collaborative Transformers for Grounded Situation Recognition, [Paper], [Code]

• (arXiv 2022.03) Object Memory Transformer for Object Goal Navigation, [Paper]

• (arXiv 2022.03) Brain-inspired Multilayer Perceptron with Spiking Neurons, [Paper], [Code]

• (arXiv 2022.03) HandOccNet: Occlusion-Robust 3D Hand Mesh Estimation Network, [Paper], [Code]

• (arXiv 2022.03) REGTR: End-to-end Point Cloud Correspondences with Transformers, [Paper], [Code]

• (arXiv 2022.03) Automated Progressive Learning for Efficient Training of Vision Transformers, [Paper]

• (arXiv 2022.03) Stratified Transformer for 3D Point Cloud Segmentation, [Paper], [Code]

• (arXiv 2022.03) NOC-REK: Novel Object Captioning with Retrieved Vocabulary from External Knowledge, [Paper]

• (arXiv 2022.03) FACIAL EXPRESSION RECOGNITION WITH SWIN TRANSFORMER, [Paper]

• (arXiv 2022.03) Give Me Your Attention: Dot-Product Attention Considered Harmful for Adversarial Patch Robustness, [Paper]

• (arXiv 2022.03) Efficient Visual Tracking via Hierarchical Cross-Attention Transformer, [Paper], [Code]

• (arXiv 2022.03) High-Performance Transformer Tracking, [Paper], [Code]

• (arXiv 2022.03) RayTran: 3D pose estimation and shape reconstruction of multiple objects from videos with ray-traced transformers, [Paper]

• (arXiv 2022.03) Multi-modal Multi-label Facial Action Unit Detection with Transformer, [Paper]

• (arXiv 2022.03) MonoDETR: Depth-aware Transformer for Monocular 3D Object Detection, [Paper], [Code]

• (arXiv 2022.03) Text to Mesh Without 3D Supervision Using Limit Subdivision, [Paper], [Project]

• (arXiv 2022.03) GEN-VLKT: Simplify Association and Enhance Interaction Understanding for HOI Detection, [Paper], [Code]

• (arXiv 2022.03) CrossFormer: Cross Spatio-Temporal Transformer for 3D Human Pose Estimation, [Paper]

• (arXiv 2022.03) FitCLIP: Refining Large-Scale Pretrained Image-Text Models for Zero-Shot Video Understanding Tasks, [Paper], [Code]

• (arXiv 2022.03) Vision Transformer Compression with Structured Pruning and Low Rank Approximation, [Paper]

• (arXiv 2022.03) Multi-Modal Learning for AU Detection Based on Multi-Head Fused Transformers, [Paper]

• (arXiv 2022.03) MSTR: Multi-Scale Transformer for End-to-End Human-Object Interaction Detection, [Paper]

• (arXiv 2022.03) Learning Patch-to-Cluster Attention in Vision Transformer, [Paper]

• (arXiv 2022.03) Visual Prompt Tuning, [Paper]

• (arXiv 2022.03) Training-free Transformer Architecture Search, [Paper]

• (arXiv 2022.03) VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training, [Paper], [Code]

• (arXiv 2022.03) METAMORPH: LEARNING UNIVERSAL CONTROLLERS WITH TRANSFORMERS, [Paper], [Project]

• (arXiv 2022.03) A Prompt Array Keeps the Bias Away: Debiasing Vision-Language Models with Adversarial Learning, [Paper]

• (arXiv 2022.03) Reshaping Robot Trajectories Using Natural Language Commands: A Study of Multi-Modal Data Alignment Using Transformers, [Paper], [Project]

• (arXiv 2022.03) Associating Objects with Scalable Transformers for Video Object Segmentation, [Paper], [[Project]](https://github.com/z-x-yang/AOT0

• (arXiv 2022.03) HOP: History-and-Order Aware Pre-training for Vision-and-Language Navigation, [Paper], [Code]

• (arXiv 2022.03) Learning to generate line drawings that convey geometry and semantics, [Paper], [Project]

• (arXiv 2022.03) UMT: Unified Multi-modal Transformers for Joint Video Moment Retrieval and Highlight Detection, [Paper], [Code]

• (arXiv 2022.03) AIMusicGuru: Music Assisted Human Pose Correction, [Paper]

• (arXiv 2022.03) What to Hide from Your Students: Attention-Guided Masked Image Modeling, [Paper]

• (arXiv 2022.03) Towards Efficient and Elastic Visual Question Answering with Doubly Slimmable Transformer, [Paper]

• (arXiv 2022.03) ViT-FOD: A Vision Transformer based Fine-grained Object Discriminator, [Paper]

• (arXiv 2022.03) Keypoints Tracking via Transformer Networks, [Paper], [Code]

• (arXiv 2022.03) Beyond Fixation: Dynamic Window Visual Transformer, [Paper], [Code]

• (arXiv 2022.03) Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors, [Paper]

• (arXiv 2022.03) Self-supervised Video-centralised Transformer for Video Face Clustering, [Paper]

• (arXiv 2022.03) Towards Exemplar-Free Continual Learning in Vision Transformers: an Account of Attention, Functional and Weight Regularization, [Paper]

• (arXiv 2022.03) Global Tracking Transformers, [Paper], [Code]

• (arXiv 2022.03) Video Instance Segmentation via Multi-scale Spatio-temporal Split Attention Transformer, [Paper], [Code]

• (arXiv 2022.03) QS-Craft: Learning to Quantize, Scrabble and Craft for Conditional Human Motion Animation, [Paper]

• (arXiv 2022.03) Look for the Change: Learning Object States and State-Modifying Actions from Untrimmed Web Videos, [Paper], [Project]

• (arXiv 2022.03) GradViT: Gradient Inversion of Vision Transformers, [Paper], [Code]

• (arXiv 2022.03) Mask Usage Recognition using Vision Transformer with Transfer Learning and Data Augmentation, [Paper]

• (arXiv 2022.03) Under the Hood of Transformer Networks for Trajectory Forecasting, [Paper]

• (arXiv 2022.03) Open-Vocabulary DETR with Conditional Matching, [Paper]

• (arXiv 2022.03) Meta-attention for ViT-backed Continual Learning, [Paper], [Code]

• (arXiv 2022.03) CNNs and Transformers Perceive Hybrid Images Similar to Humans, [Paper], [Code]

• (arXiv 2022.03) Bailando: 3D Dance Generation by Actor-Critic GPT with Choreographic Memory, [Paper], [Code]

• (arXiv 2022.03) Affective Feedback Synthesis Towards Multimodal Text and Image Data, [Paper]

• (arXiv 2022.03) ViewFormer: NeRF-free Neural Rendering from Few Images Using Transformers, [Paper]

• (arXiv 2022.03) CLIP on Wheels: Zero-Shot Object Navigation as Object Localization and Exploration, [Paper]

• (arXiv 2022.03) Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds, [Paper], [Code]

• (arXiv 2022.03) HIPA: Hierarchical Patch Transformer for Single Image Super Resolution, [Paper]

• (arXiv 2022.03) DirecFormer: A Directed Attention in Transformer Approach to Robust Action Recognition, [Paper], [Code]

• (arXiv 2022.03) MixFormer: End-to-End Tracking with Iterative Mixed Attention, [Paper], [Code]

• (arXiv 2022.03) PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark, [Paper], [Code]

• (arXiv 2022.03) Relationformer: A Unified Framework for Image-to-Graph Generation, [Paper], [Code]

• (arXiv 2022.03) CLIP meets GamePhysics: Towards bug identification in gameplay videos using zero-shot transfer learning, [Paper], [Code]

• (arXiv 2022.03) Hyperbolic Vision Transformers: Combining Improvements in Metric Learning, [Paper], [Code]

• (arXiv 2022.03) MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer, [Paper], [Code]

• (arXiv 2022.03) Transformer-based HTR for Historical Documents, [Paper]

• (arXiv 2022.03) simCrossTrans: A Simple Cross-Modality Transfer Learning for Object Detection with ConvNets or Vision Transformers, [Paper], [Code]

• (arXiv 2022.03) End-to-End Human-Gaze-Target Detection with Transformers, [Paper]

• (arXiv 2022.03) End-to-End Video Text Spotting with Transformer, [Paper], [Code]

• (arXiv 2022.03) Open-Vocabulary One-Stage Detection with Hierarchical Visual-Language Knowledge Distillation, [Paper], [Code]

• (arXiv 2022.03) V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer, [Paper]

• (arXiv 2022.03) LocATe: End-to-end Localization of Actions in 3D with Transformers, [Paper]

• (arXiv 2022.03) AnoViT: Unsupervised Anomaly Detection and Localization with Vision Transformer-based Encoder-Decoder, [Paper]

• (arXiv 2022.03) ViM: Out-Of-Distribution with Virtual-logit Matching, [Paper], [Code]

• (arXiv 2022.03) ScalableViT: Rethinking the Context-oriented Generalization of Vision Transformer, [Paper]

• (arXiv 2022.03) Iwin: Human-Object Interaction Detection via Transformer with Irregular Windows, [Paper]

• (arXiv 2022.03) Vision Transformer with Convolutions Architecture Search, [Paper]

• (arXiv 2022.03) Cascade Transformers for End-to-End Person Search, [Paper], [Code]

• (arXiv 2022.03) CodedVTR: Codebook-based Sparse Voxel Transformer with Geometric Guidance, [Paper]

• (arXiv 2022.03) MatchFormer: Interleaving Attention in Transformers for Feature Matching, [Paper], [Code]

• (arXiv 2022.03) Local-Global Context Aware Transformer for Language-Guided Video Segmentation, [Paper], [Code]

• (arXiv 2022.03) Three things everyone should know about Vision Transformers, [Paper]

• (arXiv 2022.03) Are Vision Transformers Robust to Spurious Correlations? [Paper], [Code]

• (arXiv 2022.03) MUTUAL GENERATIVE TRANSFORMER LEARNING FOR CROSS-VIEW GEO-LOCALIZATION, [Paper]

• (arXiv 2022.03) DU-VLG: Unifying Vision-and-Language Generation via Dual Sequence-to-Sequence Pre-training, [Paper]

• (arXiv 2022.03) Semantic-aligned Fusion Transformer for One-shot Object Detection, [Paper]

• (arXiv 2022.03) UNIMO-2: End-to-End Unified Vision-Language Grounded Learning, [Paper], [Code]

• (arXiv 2022.03) Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning, [Paper], [Code]

• (arXiv 2022.03) One-Shot Adaptation of GAN in Just One CLIP, [Paper]

• (arXiv 2022.03) PanoFormer: Panorama Transformer for Indoor 360° Depth Estimation, [Paper]

• (arXiv 2022.03) PreTR: Spatio-Temporal Non-Autoregressive Trajectory Prediction Transformer, [Paper]

• (arXiv 2022.03) Look Outside the Room: Synthesizing A Consistent Long-Term 3D Scene Video from A Single Image, [Paper], [Code]

• (arXiv 2022.03) Transframer: Arbitrary Frame Prediction with Generative Models, [Paper]

• (arXiv 2022.03) Towards Data-Efficient Detection Transformers, [Paper], [Code]

• (arXiv 2022.03) Bi-directional Object-Context Prioritization Learning for Saliency Ranking, [Paper], [Code]

• (arXiv 2022.03) PATCH-FOOL: ARE VISION TRANSFORMERS ALWAYS ROBUST AGAINST ADVERSARIAL PERTURBATIONS? [Paper], [Code]

• (arXiv 2022.03) WegFormer: Transformers for Weakly Supervised Semantic Segmentation, [Paper]

• (arXiv 2022.03) Open Set Recognition using Vision Transformer with an Additional Detection Head, [Paper], [Code]

• (arXiv 2022.03) UNIFIED VISUAL TRANSFORMER COMPRESSION, [Paper], [Code]

• (arXiv 2022.03) Towards Practical Certifiable Patch Defense with Vision Transformer, [Paper]

• (arXiv 2022.03) EDTER: Edge Detection with Transformer, [Paper], [Code]

• (arXiv 2022.03) ActFormer: A GAN Transformer Framework towards General Action-Conditioned 3D Human Motion Generation, [Paper]

• (arXiv 2022.03) Rich CNN-Transformer Feature Aggregation Networks for Super-Resolution, [Paper]

• (arXiv 2022.03) Revitalize Region Feature for Democratizing Video-Language Pre-training, [Paper], [Code]

• (arXiv 2022.03) Inverted Pyramid Multi-task Transformer for Dense Scene Understanding, [Paper]

• (arXiv 2022.03) Smoothing Matters: Momentum Transformer for Domain Adaptive Semantic Segmentation, [Paper], [Code]

• (arXiv 2022.03) Style Transformer for Image Inversion and Editing, [Paper], [Code]

• (arXiv 2022.03) MotionCLIP: Exposing Human Motion Generation to CLIP Space, [Paper], [Project]

• (arXiv 2022.03) The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of Redundancy, [Paper], [Code]

• (arXiv 2022.03) Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation, [Paper]

• (arXiv 2022.03) Sparse Local Patch Transformer for Robust Face Alignment and Landmarks Inherent Relation Learning, [Paper], [Code]

• (arXiv 2022.03) Joint CNN and Transformer Network via weakly supervised Learning for efficient crowd counting, [Paper]

• (arXiv 2022.03) DFTR: Depth-supervised Hierarchical Feature Fusion Transformer for Salient Object Detection, [Paper]

• (arXiv 2022.03) DATR: Domain-adaptive transformer for multi-domain landmark detection, [Paper]

• (arXiv 2022.03) EventFormer: AU Event Transformer for Facial Action Unit Event Detection, [Paper]

• (arXiv 2022.03) Accelerating DETR Convergence via Semantic-Aligned Matching, [Paper], [Code]

• (arXiv 2022.03) All in One: Exploring Unified Video-Language Pre-training, [Paper], [Code]

• (arXiv 2022.03) CLIP Models are Few-shot Learners: Empirical Studies on VQA and Visual Entailment, [Paper]

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• (arXiv 2021.12) Efficient Visual Tracking with Exemplar Transformers, [Paper], [Code]

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• (arXiv 2021.12) Distilled Dual-Encoder Model for Vision-Language Understanding, [Paper], [Code]

• (arXiv 2021.12) Dense Video Captioning Using Unsupervised Semantic Information, [Paper], [Code]

• (arXiv 2021.12) Looking Outside the Box to Ground Language in 3D Scenes, [Paper], [Code]

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• (arXiv 2021.12) Masked Feature Prediction for Self-Supervised Visual Pre-Training, [Paper]

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• (arXiv 2021.12) Co-training Transformer with Videos and Images Improves Action Recognition, [Paper]

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• (arXiv 2021.12) AdaViT: Adaptive Tokens for Efficient Vision Transformer, [Paper]

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• (arXiv 2021.12) Towards a Unified Foundation Model: Jointly Pre-Training Transformers on Unpaired Images and Text, [Paper]

• (arXiv 2021.12) Deep ViT Features as Dense Visual Descriptors, [Paper], [Project]

• (arXiv 2021.12) Geometry-Contrastive Transformer for Generalized 3D Pose Transfer, [Paper], [Code]

• (arXiv 2021.12) Temporal Transformer Networks with Self-Supervision for Action Recognition, [Paper]

• (arXiv 2021.12) COMPOSER: Compositional Learning of Group Activity in Videos, [Paper]

• (arXiv 2021.12) Short and Long Range Relation Based Spatio-Temporal Transformer for Micro-Expression Recognition, [Paper]

• (arXiv 2021.12) Improving and Diagnosing Knowledge-Based Visual Question Answering via Entity Enhanced Knowledge Injection, [Paper]

• (arXiv 2021.12) SVIP: Sequence VerIfication for Procedures in Videos, [Paper]

• (arXiv 2021.12) Improving Vision Transformers for Incremental Learning, [Paper]

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• (arXiv 2021.12) Embracing Single Stride 3D Object Detector with Sparse Transformer, [Paper], [Code]

• (arXiv 2021.12) PartGlot: Learning Shape Part Segmentation from Language Reference Games, [Paper]

• (arXiv 2021.12) Pedestrian Trajectory Prediction via Spatial Interaction Transformer Network, [Paper]

• (arXiv 2021.12) LEARNING SEMANTIC-ALIGNED FEATURE REPRESENTATION FOR TEXT-BASED PERSON SEARCH, [Paper]

• (arXiv 2021.12) L-Verse: Bidirectional Generation Between Image and Text, [Paper]

• (arXiv 2021.12) SELF-ATTENTION DOES NOT NEED O(n^2) MEMORY, [Paper]

• (arXiv 2021.12) Are Vision Transformers Robust to Patch Perturbations? [Paper]

• (arXiv 2021.12) Mesa: A Memory-saving Training Framework for Transformers, [Paper], [Code]

• (arXiv 2021.12) Injecting Semantic Concepts into End-to-End Image Captioning, [Paper]

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• (arXiv 2021.12) FaceFormer: Speech-Driven 3D Facial Animation with Transformers, [Paper]

• (arXiv 2021.12) Rethinking the Two-Stage Framework for Grounded Situation Recognition, [Paper], [Code]

• (arXiv 2021.12) CLIP2StyleGAN: Unsupervised Extraction of StyleGAN Edit Directions, [Paper]

• (arXiv 2021.12) Couplformer: Rethinking Vision Transformer with Coupling Attention Map, [Paper]

• (arXiv 2021.12) Unified Multimodal Pre-training and Prompt-based Tuning for Vision-Language Understanding and Generation, [Paper]

• (arXiv 2021.12) Visual Transformers with Primal Object Queries for Multi-Label Image Classification, [Paper]

• (arXiv 2021.12) Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training, [Paper], [Code]

• (arXiv 2021.12) MS-TCT: Multi-Scale Temporal ConvTransformer for Action Detection, [Paper]

• (arXiv 2021.12) Grounded Language-Image Pre-training, [Paper], [Code]

• (arXiv 2021.12) U^2-Former: A Nested U-shaped Transformer for Image Restoration, [Paper]

• (arXiv 2021.12) ADAPTIVE CHANNEL ENCODING TRANSFORMER FOR POINT CLOUD ANALYSIS, [Paper]

• (arXiv 2021.12) Pose-guided Feature Disentangling for Occluded Person Re-identification Based on Transformer, [Paper], [Code]

• (arXiv 2021.12) VT-CLIP: Enhancing Vision-Language Models with Visual-guided Texts, [Paper]

• (arXiv 2021.12) PointCLIP: Point Cloud Understanding by CLIP, [Paper], [Code]

• (arXiv 2021.12) Learning Tracking Representations via Dual-Branch Fully Transformer Networks, [Paper], [Code]

• (arXiv 2021.12) DYNAMIC TOKEN NORMALIZATION IMPROVES VISION TRANSFORMER, [Paper], [Code]

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• (arXiv 2021.12) Learning Generalizable Vision-Tactile Robotic Grasping Strategy for Deformable Objects via Transformer, [Paper], [Code]

• (arXiv 2021.12) Hformer: Hybrid CNN-Transformer for Fringe Order Prediction in Phase Unwrapping of Fringe Projection, [Paper]

• (arXiv 2021.12) Pre-training and Fine-tuning Transformers for fMRI Prediction Tasks, [Paper]

• (arXiv 2021.12) Transformer based trajectory prediction, [Paper]

• (arXiv 2021.12) Evaluating Transformers for Lightweight Action Recognition, [Paper]

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• (arXiv 2021.12) DoodleFormer: Creative Sketch Drawing with Transformers, [Paper]

• (arXiv 2021.12) Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning, [Paper]

• (arXiv 2021.12) AUDIO-VISUAL SYNCHRONISATION IN THE WILD, [Paper], [Project]

• (arXiv 2021.12) Classification-Then-Grounding: Reformulating Video Scene Graphs as Temporal Bipartite Graphs, [Paper]

• (arXiv 2021.12) Garment4D: Garment Reconstruction from Point Cloud Sequences, [Paper], [Code]

• (arXiv 2021.12) Locally Shifted Attention**** With Early Global Integration, [Paper], [Code]

• (arXiv 2021.12) BLT: Bidirectional Layout Transformer for Controllable Layout Generation, [Paper]

• (arXiv 2021.12) PE-former: Pose Estimation Transformer, [Paper], [Project]

• (arXiv 2021.12) HairCLIP: Design Your Hair by Text and Reference Image, [Paper], [Project]

• (arXiv 2021.12) CLIP-NeRF: Text-and-Image Driven Manipulation of Neural Radiance Fields, [Paper], [Code]

• (arXiv 2021.12) A Bilingual, Open World Video Text Dataset and End-to-end Video Text Spotter with Transformer, [Paper], [Code], [Dataset]

• (arXiv 2021.12) DualFormer: Local-Global Stratified Transformer for Efficient Video Recognition, [Paper], [Code]

• (arXiv 2021.12) Recurrent Glimpse-based Decoder for Detection with Transformer, [Paper], [Code]

• (arXiv 2021.12) Fast Point Transformer, [Paper]

• (arXiv 2021.12) Assistive Tele-op: Leveraging Transformers to Collect Robotic Task Demonstrations, [Paper], [Project]

• (arXiv 2021.12) Cross-Modality Fusion Transformer for Multispectral Object Detection, [Paper]

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• (arXiv 2021.12) Transformer-Based Approach for Joint Handwriting and Named Entity Recognition in Historical documents, [Paper]

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• (arXiv 2021.12) Everything at Once – Multi-modal Fusion Transformer for Video Retrieval, [Paper]

• (arXiv 2021.12) Prompting Visual-Language Models for Efficient Video Understanding, [Paper], [Project]

• (arXiv 2021.12) FLAVA: A Foundational Language And Vision Alignment Model, [Paper]

• (arXiv 2021.12) Embedding Arithmetic for Text-driven Image Transformation, [Paper]

• (arXiv 2021.12) LAVT: Language-Aware Vision Transformer for Referring Image Segmentation, [Paper]

• (arXiv 2021.12) Look at What I’m Doing: Self-Supervised Spatial Grounding of Narrations in Instructional Videos, [Paper], [Project]

• (arXiv 2021.12) Uni-Perceiver: Pre-training Unified Architecture for Generic Perception for Zero-shot and Few-shot Tasks, [Paper]

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• (arXiv 2021.12) Self-supervised Video Transformer, [Paper], [Code]

• (arXiv 2021.12) OW-DETR: Open-world Detection Transformer, [Paper]

• (arXiv 2021.12) Zero-Shot Text-Guided Object Generation with Dream Fields, [Paper], [Project]

• (arXiv 2021.12) Video-Text Pre-training with Learned Regions, [Paper], [Code]

• (arXiv 2021.12) MTFNet: Mutual-Transformer Fusion Network for RGB-D Salient Object Detection, [Paper]

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• (arXiv 2021.12) Object-Centric Unsupervised Image Captioning, [Paper]

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• (arXiv 2021.12) Visual-Semantic Transformer for Scene Text Recognition, [Paper]

• (arXiv 2021.12) Differentiable Spatial Planning using Transformers, [Paper], [Project]

• (arXiv 2021.12) Improved Multiscale Vision Transformers for Classification and Detection, [Paper]

• (arXiv 2021.12) Masked-attention Mask Transformer for Universal Image Segmentation, [Paper], [Code]

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• (arXiv 2021.12) Human-Object Interaction Detection via Weak Supervision, [Paper]

• (arXiv 2021.12) Learning Transformer Features for Image Quality Assessment, [Paper]

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• (arXiv 2021.12) Multi-View Stereo with Transformer, [Paper]

• (arXiv 2021.12) VoRTX: Volumetric 3D Reconstruction With Transformers for Voxelwise View Selection and Fusion, [Paper], [Code]

• (arXiv 2021.12) Object-aware Video-language Pre-training for Retrieval, [Paper], [Code]

• (arXiv 2021.11) Multi-modal Transformers Excel at Class-agnostic Object Detection, [Paper], [Code]

• (arXiv 2021.11) Predict, Prevent, and Evaluate: Disentangled Text-Driven Image Manipulation Empowered by Pre-Trained Vision-Language Model, [Paper]

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• (arXiv 2021.11) PolyViT: Co-training Vision Transformers on Images, Videos and Audio, [Paper]

• (arXiv 2021.11) SWAT: Spatial Structure Within and Among Tokens, [Paper]

• (arXiv 2021.11) ADAPTIVE FOURIER NEURAL OPERATORS: EFFICIENT TOKEN MIXERS FOR TRANSFORMERS, [Paper]

• (arXiv 2021.11) DyTox: Transformers for Continual Learning with DYnamic TOken eXpansion, [Paper], [Code]

• (arXiv 2021.11) DABS: A Domain-Agnostic Benchmark for Self-Supervised Learning, [Paper], [Code]

• (arXiv 2021.11) Ice hockey player identification via transformers, [Paper]

• (arXiv 2021.11) DBIA: Data-free Backdoor Injection Attack against Transformer Networks, [Paper], [Code]

• (arXiv 2021.11) Sparse Fusion for Multimodal Transformers, [Paper]

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• (arXiv 2021.11) Self-Supervised Pre-Training for Transformer-Based Person Re-Identification, [Paper], [Code]

• (arXiv 2021.11) DISCRETE REPRESENTATIONS STRENGTHEN VISION TRANSFORMER ROBUSTNESS, [Paper]

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• (arXiv 2021.11) Semi-Supervised Vision Transformers, [Paper]

• (arXiv 2021.11) CpT: Convolutional Point Transformer for 3D Point Cloud Processing, [Paper]

• (arXiv 2021.11) ZERO-SHOT CERTIFIED DEFENSE AGAINST ADVERSARIAL PATCHES WITH VISION TRANSFORMERS, [Paper]

• (arXiv 2021.11) PointMixer: MLP-Mixer for Point Cloud Understanding, [Paper]

• (arXiv 2021.11) MetaFormer is Actually What You Need for Vision, [Paper], [Code]

• (arXiv 2021.11) Florence: A New Foundation Model for Computer Vision, [Paper]

• (arXiv 2021.11) Benchmarking Detection Transfer Learning with Vision Transformers, [Paper]

• (arXiv 2021.11) Learning to Compose Visual Relations, [Paper], [Project]

• (arXiv 2021.11) REFERENCE-BASED MAGNETIC RESONANCE IMAGE RECONSTRUCTION USING TEXTURE TRANSFORMER, [Paper]

• (arXiv 2021.11) Induce, Edit, Retrieve: Language Grounded Multimodal Schema for Instructional Video Retrieval, [Paper]

• (arXiv 2021.11) Swin Transformer V2: Scaling Up Capacity and Resolution, [Paper], [Code]

• (arXiv 2021.11) SimMIM: A Simple Framework for Masked Image Modeling, [Paper], [Code]

• (arXiv 2021.11) Restormer: Efficient Transformer for High-Resolution Image Restoration, [Paper], [Code]

• (arXiv 2021.11) Simple but Effective: CLIP Embeddings for Embodied AI, [Paper]

• (arXiv 2021.11) ClipCap: CLIP Prefix for Image Captioning, [Paper], [Code]

• (arXiv 2021.11) TransMix: Attend to Mix for Vision Transformers, [Paper], [Code]

• (arXiv 2021.11) TRIG: Transformer-Based Text Recognizer with Initial Embedding Guidance, [Paper]

• (arXiv 2021.11) Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts, [Paper], [Code]

• (arXiv 2021.11) Explainable Semantic Space by Grounding Language to Vision with Cross-Modal Contrastive Learning, [Paper], [Code]

• (arXiv 2021.11) Semantically Grounded Object Matching for Robust Robotic Scene Rearrangement, [Paper], [Code]

• (arXiv 2021.11) Tracking People with 3D Representations, [Paper], [Code]

• (arXiv 2021.11) LiT: Zero-Shot Transfer with Locked-image Text Tuning, [Paper]

• (arXiv 2021.11) FILIP: FINE-GRAINED INTERACTIVE LANGUAGE-IMAGE PRE-TRAINING, [Paper]

• (arXiv 2021.11) Graph Relation Transformer: Incorporating pairwise object features into the Transformer architecture, [Paper], [Code]

• (arXiv 2021.11) Attention Approximates Sparse Distributed Memory, [Paper]

• (arXiv 2021.11) SLICED RECURSIVE TRANSFORMER, [Paper], [Code]

• (arXiv 2021.11) HYBRID BYOL-VIT: EFFICIENT APPROACH TO DEAL WITH SMALL DATASETS, [Paper]

• (arXiv 2021.11) Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling, [Paper], [Code]

• (arXiv 2021.11) Improving Visual Quality of Image Synthesis by A Token-based Generator with Transformers, [Paper]

• (arXiv 2021.11) StyleCLIPDraw: Coupling Content and Style in Text-to-Drawing Synthesis, [Paper], [Code]

• (arXiv 2021.11) Revisiting spatio-temporal layouts for compositional action recognition, [Paper], [Code]

• (arXiv 2021.11) PatchGame: Learning to Signal Mid-level Patches in Referential Games, [Paper], [Code]

• (arXiv 2021.11) CAN VISION TRANSFORMERS PERFORM CONVOLUTION? [Paper]

• (arXiv 2021.11) Livestock Monitoring with Transformer, [Paper]

• (arXiv 2021.11) With a Little Help from my Temporal Context: Multimodal Egocentric Action Recognition, [Paper], [Code]

• (arXiv 2021.11) IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning, [Paper], [Project]

• (arXiv 2021.11) BoxeR: Box-Attention for 2D and 3D Transformers, [Paper]

• (arXiv 2021.11) VLDeformer: Vision-Language Decomposed Transformer for Fast Cross-Modal Retrieval, [Paper]

• (arXiv 2021.11) Multi-Person 3D Motion Prediction with Multi-Range Transformers, [Paper], [Code]

• (arXiv 2021.11) Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations, [Paper], [Project]

• (arXiv 2021.11) Global Interaction Modelling in Vision Transformer via Super Tokens, [Paper]

• (arXiv 2021.11) ML-Decoder: Scalable and Versatile Classification Head, [Paper], [Code]

• (arXiv 2021.11) Exploiting Both Domain-specific and Invariant Knowledge via a Win-win Transformer for Unsupervised Domain Adaptation, [Paper]

• (arXiv 2021.11) SWINBERT: End-to-End Transformers with Sparse Attention for Video Captioning, [Paper]

• (arXiv 2021.11) Amortized Prompt: Lightweight Fine-Tuning for CLIP in Domain Generalization, [Paper]

• (arXiv 2021.11) Universal Captioner: Long-Tail Vision-and-Language Model Training through Content-Style Separation, [Paper]

• (arXiv 2021.11) Sparse is Enough in Scaling Transformers, [Paper]

• (arXiv 2021.11) An implementation of the “Guess who?” game using CLIP, [Paper], [Code]

• (arXiv 2021.11) HEAT: Holistic Edge Attention Transformer for Structured Reconstruction, [Paper]

• (arXiv 2021.11) A Unified Pruning Framework for Vision Transformers, [Paper]

• (arXiv 2021.11) Pyramid Adversarial Training Improves ViT Performance, [Paper]

• (arXiv 2021.11) AssistSR: Affordance-centric Question-driven Video Segment Retrieval, [Paper], [Code & Data]

• (arXiv 2021.11) DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation, [Paper], [Code]

• (arXiv 2021.11) , [Paper]

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• (arXiv 2021.11) ATS: Adaptive Token Sampling For Efficient Vision Transformers, [Paper]

• (arXiv 2021.11) CLIP Meets Video Captioners: Attribute-Aware Representation Learning Promotes Accurate Captioning, [Paper]

• (arXiv 2021.11) CRIS: CLIP-Driven Referring Image Segmentation, [Paper]

• (arXiv 2021.11) Shunted Self-Attention via Multi-Scale Token Aggregation, [Paper], [Code]

• (arXiv 2021.11) MC-SSL0.0: Towards Multi-Concept Self-Supervised Learning, [Paper]

• (arXiv 2021.11) TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditions, [Paper], [Code]

• (arXiv 2021.11) Searching the Search Space of Vision Transformer, [Paper], [Code]

• (arXiv 2021.11) TransMVSNet: Global Context-aware Multi-view Stereo Network with Transformers, [Paper], [Code]

• (arXiv 2021.11) Recurrent Vision Transformer for Solving Visual Reasoning Problems, [Paper]

• (arXiv 2021.11) Video Frame Interpolation Transformer, [Paper]

• (arXiv 2021.11) FQ-ViT: Fully Quantized Vision Transformer without Retraining, [Paper], [Code]

• (arXiv 2021.11) LAFITE : Towards Language-Free Training for Text-to-Image Generation, [Paper]

• (arXiv 2021.11) SPARSE DETR: EFFICIENT END-TO-END OBJECT DETECTION WITH LEARNABLE SPARSITY, [Paper], [Code]

• (arXiv 2021.11) End-to-End Referring Video Object Segmentation with Multimodal Transformers, [Paper], [Code]

• (arXiv 2021.11) Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling, [Paper], [Code]

• (arXiv 2021.11) Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic, [Paper], [Code]

• (arXiv 2021.11) Blended Diffusion for Text-driven Editing of Natural Images, [Paper], [Code]

• (arXiv 2021.11) Mask Transfiner for High-Quality Instance Segmentation, [Paper], [Code]

• (arXiv 2021.11) MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation, [Paper], [Code]

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• (arXiv 2021.11) Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes, [Paper], [COde]

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• (arXiv 2021.08) Guiding Query Position and Performing Similar Attention for Transformer-Based Detection Heads, [Paper]

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• (arXiv 2021.08) TransFER: Learning Relation-aware Facial Expression Representations with Transformers, [[Paper]](https://