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

The transformer is now a basic component, adopted in nearly all AI models. Keep updated --> updated irregularly.

New Hope: LLM-in-Vision

• ChatGPT for Robotics: Design Principles and Model Abilities, [Paper], [Code]

• DIFFUSIONDB [Page], [Paper]

• LAION-5B [Page], [Paper]

• LAVIS [Page], [Paper]

• Imagen Video [Page], [Paper]

• Phenaki [Page], [Paper]

• DREAMFUSION [Page], [Paper]

• MAKE-A-VIDEO [Page], [Paper]

• Stable Difffusion [Page], [Paper]

• NUWA-Infinity [Page], [Paper]

• Parti [Page], [Code]

• Imagen [Page], [Paper]

• Gato: A Generalist Agent, [Paper]

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

• 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]

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

• 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 2023.2) TRANSFORMER-BASED SENSOR FUSION FOR AUTONOMOUS DRIVING: A SURVEY, [Paper], [Page]

• (arXiv 2023.2) Deep Learning for Video-Text Retrieval: a Review, [Paper]

• (arXiv 2023.2) Large-scale Multi-Modal Pre-trained Models: A Comprehensive Survey, [Paper]

• (arXiv 2023.2) Transformer-based Generative Adversarial Networks in Computer Vision: A Comprehensive Survey, [Paper]

• (arXiv 2023.2) Knowledge Distillation in Vision Transformers: A Critical Review, [Paper]

• (arXiv 2023.2) A Survey on Efficient Training of Transformers, [Paper]

• (arXiv 2023.1) ChatGPT is not all you need. A State of the Art Review of large Generative AI models, [Paper]

• (arXiv 2022.12) Transformers in Action Recognition: A Review on Temporal Modeling, [Paper]

• (arXiv 2022.11) Vision Transformers in Medical Imaging: A Review, [Paper]

• (arXiv 2022.11) A survey on knowledge-enhanced multimodal learning, [Paper]

• (arXiv 2022.10) Vision-Language Pre-training: Basics, Recent Advances, and Future Trends, [Paper]

• (arXiv 2022.10) A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective, [Paper]

• (arXiv 2022.09) VISION TRANSFORMERS FOR ACTION RECOGNITION: A SURVEY, [Paper]

• (arXiv 2022.09) Transformers in Remote Sensing: A Survey, [Paper], [Code]

• (arXiv 2022.08) 3D Vision with Transformers: A Survey, [Paper], [Code]

• (arXiv 2022.08) A Survey on Masked Autoencoder for Self-supervised Learning in Vision and Beyond, [Paper]

• (arXiv 2022.07) Vision Transformers: State of the Art and Research Challenges, [Paper]

• (arXiv 2022.07) SELF-SUPERVISED LEARNING FOR VIDEOS: A SURVEY, [Paper]

• (arXiv 2022.06) Multimodal Learning with Transformers: A Survey, [Paper]

• (arXiv 2022.05) Vision Transformer: Vit and its Derivatives, [Paper]

• (arXiv 2022.05) Transformers in 3D Point Clouds: A Survey, [Paper]

• (arXiv 2022.04) Visual Attention Methods in Deep Learning: An In-Depth Survey, [Paper]

• (arXiv 2022.04) Vision-and-Language Pretrained Models: A Survey, [Paper]

• (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 2023.8) VL-PET: Vision-and-Language Parameter-Efficient Tuning via Granularity Control, [Paper], [Project]

• (arXiv 2023.5) Understanding Gaussian Attention Bias of Vision Transformers Using Effective Receptive Fields, [Paper]

• (arXiv 2023.3) Query-Dependent Video Representation for Moment Retrieval and Highlight Detection, [Paper], [Code]

• (arXiv 2023.2) Open-domain Visual Entity Recognition: Towards Recognizing Millions of Wikipedia Entities, [Paper]

• (arXiv 2023.2) KS-DETR: Knowledge Sharing in Attention Learning for Detection Transformer, [Paper], [Code]

• (arXiv 2023.2) HUMAN MOTIONFORMER: TRANSFERRING HUMAN MOTIONS WITH VISION TRANSFORMERS, [Paper], [Code]

• (arXiv 2023.2) Aligning Text-to-Image Models using Human Feedback, [Paper]

• (arXiv 2023.2) Controlled and Conditional Text to Image Generation with Diffusion Prior, [Paper]

• (arXiv 2023.2) Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions? [Paper], [Code]

• (arXiv 2023.2) OBJECT-CENTRIC VIDEO PREDICTION VIA DECOUPLING OF OBJECT DYNAMICS AND INTERACTIONS, [Paper], [Project]

• (arXiv 2023.2) Distribution Normalization: An “Effortless” Test-Time Augmentation for Contrastively Learned Visual-language Models, [Paper], [Code]

• (arXiv 2023.2) Teaching CLIP to Count to Ten, [Paper], [Project]

• (arXiv 2023.2) Designing an Encoder for Fast Personalization of Text-to-Image Models, [Paper], [Project]

• (arXiv 2023.2) Side Adapter Network for Open-Vocabulary Semantic Segmentation, [Paper], [Code]

• (arXiv 2023.2) Learning Visual Representations via Language-Guided Sampling, [Paper]

• (arXiv 2023.2) VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion, [Paper], [Code]

• (arXiv 2023.2) Language-Driven Representation Learning for Robotics, [Paper], [Project]

• (arXiv 2023.2) A Convolutional Vision Transformer for Semantic Segmentation of Side-Scan Sonar Data, [Paper], [Code]

• (arXiv 2023.2) Lightweight Real-time Semantic Segmentation Network with Efficient Transformer and CNN, [Paper], [Code]

• (arXiv 2023.2) VIEWCO: DISCOVERING TEXT-SUPERVISED SEGMENTATION MASKS VIA MULTI-VIEW SEMANTIC CONSISTENCY, [Paper], [Code]

• (arXiv 2023.2) CertViT: Certified Robustness of Pre-Trained Vision Transformers, [Paper], [Code]

• (arXiv 2023.2) Paparazzi: A Deep Dive into the Capabilities of Language and Vision Models for Grounding Viewpoint Descriptions, [Paper]

• (arXiv 2023.2) MaskedKD: Efficient Distillation of Vision Transformers with Masked Images, [Paper]

• (arXiv 2023.2) A General Visual Representation Guided Framework with Global Affinity for Weakly Supervised Salient Object Detection, [Paper]

• (arXiv 2023.2) ViTA: A Vision Transformer Inference Accelerator for Edge Applications, [Paper]

• (arXiv 2023.2) Video Action Recognition Collaborative Learning with Dynamics via PSO-ConvNet Transformer, [Paper], [Code]

• (arXiv 2023.2) A Pilot Evaluation of ChatGPT and DALL-E 2 on Decision Making and Spatial Reasoning, [Paper]

• (arXiv 2023.2) StyLIP: Multi-Scale Style-Conditioned Prompt Learning for CLIP-based Domain Generalization, [Paper]

• (arXiv 2023.2) Meta Style Adversarial Training for Cross-Domain Few-Shot Learning, [Paper]

• (arXiv 2023.2) HYNETER: HYBRID NETWORK TRANSFORMER FOR OBJECT DETECTION, [Paper]

• (arXiv 2023.2) STOA-VLP: Spatial-Temporal Modeling of Object and Action for Video-Language Pre-training, [Paper]

• (arXiv 2023.2) Constraint and Union for Partially-Supervised Temporal Sentence Grounding, [Paper]

• (arXiv 2023.2) STB-VMM: Swin Transformer Based Video Motion Magnification, [Paper]

• (arXiv 2023.2) Fashion Image Retrieval with Multi-Granular Alignment, [Paper]

• (arXiv 2023.2) LayoutDiffuse: Adapting Foundational Diffusion Models for Layout-to-Image Generation, [Paper]

• (arXiv 2023.2) CK-Transformer: Commonsense Knowledge Enhanced Transformers for Referring Expression Comprehension, [Paper], [Code]

• (arXiv 2023.2) MaskSketch: Unpaired Structure-guided Masked Image Generation, [Paper]

• (arXiv 2023.2) Single Motion Diffusion, [Paper], [Code]

• (arXiv 2023.2) Tri-Perspective View for Vision-Based 3D Semantic Occupancy Prediction, [Paper], [Code]

• (arXiv 2023.2) ANSEL Photobot: A Robot Event Photographer with Semantic Intelligence, [Paper]

• (arXiv 2023.2) ForceFormer: Exploring Social Force and Transformer for Pedestrian Trajectory Prediction, [Paper]

• (arXiv 2023.2) Video Probabilistic Diffusion Models in Projected Latent Space, [Paper]

• (arXiv 2023.2) Dataset Interfaces: Diagnosing Model Failures Using Controllable Counterfactual Generation, [Paper], [Code]

• (arXiv 2023.2) Learning to Substitute Ingredients in Recipes, [Paper]

• (arXiv 2023.2) Energy Transformer, [Paper]

• (arXiv 2023.2) Efficiency 360: Efficient Vision Transformers, [Paper]

• (arXiv 2023.2) A-la-carte Prompt Tuning (APT): Combining Distinct Data Via Composable ` Prompting, [Paper]

• (arXiv 2023.2) Effective Data Augmentation With Diffusion Models, [Paper], [Project]

• (arXiv 2023.2) PRedItOR: Text Guided Image Editing with Diffusion Prior, [Paper]

• (arXiv 2023.2) TcGAN: Semantic-Aware and Structure-Preserved GANs with Individual Vision Transformer for Fast Arbitrary One-Shot Image Generation, [Paper]

• (arXiv 2023.2) Hierarchical Cross-modal Transformer for RGB-D Salient Object Detection, [Paper]

• (arXiv 2023.2) MINOTAUR: Multi-task Video Grounding From Multimodal Queries, [Paper]

• (arXiv 2023.2) Towards Efficient Visual Adaption via Structural Re-parameterization, [Paper], [Code]

• (arXiv 2023.2) Efficient 3D Object Reconstruction using Visual Transformers, [Paper]

• (arXiv 2023.2) Retrieval-augmented Image Captioning, [Paper]

• (arXiv 2023.2) Robust Human Motion Forecasting using Transformer-based Model, [Paper]

• (arXiv 2023.2) VQ3D: Learning a 3D-Aware Generative Model on ImageNet, [Paper], [Project]

• (arXiv 2023.2) UKnow: A Unified Knowledge Protocol for Common-Sense Reasoning and Vision-Language Pre-training, [Paper], [Code]

• (arXiv 2023.2) A THEORETICAL UNDERSTANDING OF SHALLOW VISION TRANSFORMERS: LEARNING, GENERALIZATION, AND SAMPLE COMPLEXITY, [Paper]

• (arXiv 2023.2) A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models, [Paper]

• (arXiv 2023.2) Generalized Few-Shot Continual Learning with Contrastive Mixture of Adapters, [Paper], [Code]

• (arXiv 2023.2) Actional Atomic-Concept Learning for Demystifying Vision-Language Navigation, [Paper]

• (arXiv 2023.2) Towards Local Visual Modeling for Image Captioning, [Paper], [Code]

• (arXiv 2023.2) CLIP-RR: IMPROVED CLIP NETWORK FOR RELATION-FOCUSED CROSS-MODAL INFORMATION RETRIEVAL, [Paper]

• (arXiv 2023.2) Anticipating Next Active Objects for Egocentric Videos, [Paper], [Code]

• (arXiv 2023.2) UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal Modeling, [Paper], [Code]

• (arXiv 2023.2) TEAM DETR: GUIDE QUERIES AS A PROFESSIONAL TEAM IN DETECTION TRANSFORMERS, [Paper], [Code]

• (arXiv 2023.2) ConceptFusion: Open-set Multimodal 3D Mapping, [Paper], [Project]

• (arXiv 2023.2) Team Triple-Check at Factify 2: Parameter-Efficient Large Foundation Models with Feature Representations for Multi-Modal Fact Verification, [Paper], [Code]

• (arXiv 2023.2) PolyFormer: Referring Image Segmentation as Sequential Polygon Generation, [Paper]

• (arXiv 2023.2) Pose-Oriented Transformer with Uncertainty-Guided Refinement for 2D-to-3D Human Pose Estimation, [Paper]

• (arXiv 2023.2) TFormer: A Transmission-Friendly ViT Model for IoT Devices, [Paper], [Code]

• (arXiv 2023.2) Tri-Perspective View for Vision-Based 3D Semantic Occupancy Prediction, [Paper], [Code]

• (arXiv 2023.2) Adding Conditional Control to Text-to-Image Diffusion Models, [Paper], [Code]

• (arXiv 2023.2) Invariant Slot Attention: Object Discovery with Slot-Centric Reference Frames, [Paper]

• (arXiv 2023.2) IS MULTI-MODAL VISION SUPERVISION BENEFICIAL TO LANGUAGE? [Paper]

• (arXiv 2023.2) Data-Driven Stochastic Motion Evaluation and Optimization with Image by Spatially-Aligned Temporal Encoding, [Paper]

• (arXiv 2023.2) Scaling Vision Transformers to 22 Billion Parameters, [Paper]

• (arXiv 2023.2) Adapting Pre-trained Vision Transformers from 2D to 3D through Weight Inflation Improves Medical Image Segmentation, [Paper], [Code]

• (arXiv 2023.2) Mitigating Bias in Visual Transformers via Targeted Alignment, [Paper]

• (arXiv 2023.2) IH-ViT: Vision Transformer-based Integrated Circuit Appearance Defect Detection, [Paper]

• (arXiv 2023.2) Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning, [Paper]

• (arXiv 2023.2) Learning by Asking for Embodied Visual Navigation and Task Completion, [Paper]

• (arXiv 2023.2) Reversible Vision Transformers, [Paper], [Code1], [Code2]

• (arXiv 2023.2) Neural Congealing: Aligning Images to a Joint Semantic Atlas, [Paper], [Project]

• (arXiv 2023.2) Adversarial Prompting for Black Box Foundation Models, [Paper]

• (arXiv 2023.2) Understanding Why ViT Trains Badly on Small Datasets: An Intuitive Perspective, [Paper], [Code]

• (arXiv 2023.2) CROSS-LAYER RETROSPECTIVE RETRIEVING VIA LAYER ATTENTION, [Paper], [Code]

• (arXiv 2023.2) Convolutional Neural Networks Trained to Identify Words Provide a Good Account of Visual Form Priming Effects, [Paper]

• (arXiv 2023.2) Zero-shot Generation of Coherent Storybook from Plain Text Story using Diffusion Models, [Paper]

• (arXiv 2023.2) OSRT: Omnidirectional Image Super-Resolution with Distortion-aware Transformer, [Paper]

• (arXiv 2023.2) Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrieval, [Paper], [Code]

• (arXiv 2023.2) SimCon Loss with Multiple Views for Text Supervised Semantic Segmentation, [Paper]

• (arXiv 2023.2) PhysFormer++: Facial Video-based Physiological Measurement with SlowFast Temporal Difference Transformer, [Paper]

• (arXiv 2023.2) Scaling Self-Supervised End-to-End Driving with Multi-View Attention Learning, [Paper]

• (arXiv 2023.2) HumanMAC: Masked Motion Completion for Human Motion Prediction, [Paper], [Project]

• (arXiv 2023.2) LAMPP: Language Models as Probabilistic Priors for Perception and Action, [Paper]

• (arXiv 2023.2) Zero-Shot Robot Manipulation from Passive Human Videos, [Paper], [Project]

• (arXiv 2023.2) MixFormer: End-to-End Tracking with Iterative Mixed Attention, [Paper], [Code]

• (arXiv 2023.2) LexLIP: Lexicon-Bottlenecked Language-Image Pre-Training for Large-Scale Image-Text Retrieval, [Paper]

• (arXiv 2023.2) V1T: large-scale mouse V1 response prediction using a Vision Transformer, [Paper]

• (arXiv 2023.2) AIM: ADAPTING IMAGE MODELS FOR EFFICIENT VIDEO ACTION RECOGNITION, [Paper], [Project]

• (arXiv 2023.2) KDEformer: Accelerating Transformers via Kernel Density Estimation, [Paper], [Code]

• (arXiv 2023.2) Semantic-Guided Image Augmentation with Pre-trained Models, [Paper]

• (arXiv 2023.2) X-ReID: Cross-Instance Transformer for Identity-Level Person Re-Identification, [Paper]

• (arXiv 2023.2) MOMA: Distill from Self-Supervised Teachers, [Paper]

• (arXiv 2023.2) Learning to Agree on Vision Attention for Visual Commonsense Reasoning, [Paper]

• (arXiv 2023.2) Efficient End-to-End Video Question Answering with Pyramidal Multimodal Transformer, [Paper], [Code]

• (arXiv 2023.2) LipFormer: Learning to Lipread Unseen Speakers based on Visual-Landmark Transformers, [Paper]

• (arXiv 2023.2) Oscillation-free Quantization for Low-bit Vision Transformers, [Paper]

• (arXiv 2023.2) Design Booster: A Text-Guided Diffusion Model for Image Translation with Spatial Layout Preservation, [Paper]

• (arXiv 2023.2) Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining, [Paper], [Code]

• (arXiv 2023.2) Leaving Reality to Imagination: Robust Classification via Generated Datasets, [Paper], [Code]

• (arXiv 2023.2) CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets, [Paper], [Code]

• (arXiv 2023.2) Zero-shot Image-to-Image Translation, [Paper], [Project]

• (arXiv 2023.2) Learning a Fourier Transform for Linear Relative Positional Encodings in Transformers, [Paper]

• (arXiv 2023.2) EXPLICIT BOX DETECTION UNIFIES END-TO-END MULTI-PERSON POSE ESTIMATION, [Paper], [Code]

• (arXiv 2023.2) CFFT-GAN: Cross-domain Feature Fusion Transformer for Exemplar-based Image Translation, [Paper]

• (arXiv 2023.2) DEVICE: DEpth and VIsual ConcEpts Aware Transformer for TextCaps, [Paper]

• (arXiv 2023.2) CVTNet: A Cross-View Transformer Network for Place Recognition Using LiDAR Data, [Paper], [Code]

• (arXiv 2023.2) DilateFormer: Multi-Scale Dilated Transformer for Visual Recognition, [Paper], [Code]

• (arXiv 2023.2) HDFormer: High-order Directed Transformer for 3D Human Pose Estimation, [Paper], [Code]

• (arXiv 2023.2) IC^3: Image Captioning by Committee Consensus, [Paper], [Code]

• (arXiv 2023.2) Boosting Low-Data Instance Segmentation by Unsupervised Pre-training with Saliency Prompt, [Paper]

• (arXiv 2023.2) QR-CLIP: Introducing Explicit Open-World Knowledge for Location and Time Reasoning, [Paper]

• (arXiv 2023.2) Vision Transformer-based Feature Extraction for Generalized Zero-Shot Learning, [Paper]

• (arXiv 2023.2) Multimodal Chain-of-Thought Reasoning in Language Models, [Paper], [Code]

• (arXiv 2023.2) CLIPood: Generalizing CLIP to Out-of-Distributions, [Paper]

• (arXiv 2023.2) Language Quantized AutoEncoders: Towards Unsupervised Text-Image Alignment, [Paper]

• (arXiv 2023.2) The geometry of hidden representations of large transformer models, [Paper]

• (arXiv 2023.2) Debiasing Vision-Language Models via Biased Prompts, [Paper], [Code]

• (arXiv 2023.2) COMPOSITIONAL PROMPT TUNING WITH MOTION CUES FOR OPEN-VOCABULARY VIDEO RELATION DETECTION, [Paper], [Code]

• (arXiv 2023.2) mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video, [Paper], [Code]

• (arXiv 2023.2) Transforming CLIP to an Open-vocabulary Video Model via Interpolated Weight Optimization, [Paper]

• (arXiv 2023.2) ADAPT: Action-aware Driving Caption Transformer, [Paper], [Code]

• (arXiv 2023.1) AdaPoinTr: Diverse Point Cloud Completion with Adaptive Geometry-Aware Transformers, [Paper], [Code]

• (arXiv 2023.1) EXIF as Language: Learning Cross-Modal Associations Between Images and Camera Metadata, [Paper], [Project]

• (arXiv 2023.1) Head-Free Lightweight Semantic Segmentation with Linear Transformer, [Paper], [Code]

• (arXiv 2023.1) Geometry-biased Transformers for Novel View Synthesis, [Paper], [Project]

• (arXiv 2023.1) Continual Few-Shot Learning Using HyperTransformers, [Paper]

• (arXiv 2023.1) SEMPPL: PREDICTING PSEUDO-LABELS FOR BETTER CONTRASTIVE REPRESENTATIONS, [Paper]

• (arXiv 2023.1) Learning to Summarize Videos by Contrasting Clips, [Paper]

• (arXiv 2023.1) Guiding Text-to-Image Diffusion Model Towards Grounded Generation, [Paper], [Project]

• (arXiv 2023.1) Domain Expansion of Image Generators, [Paper], [Code]

• (arXiv 2023.1) Scene-centric vs. Object-centric Image-Text Cross-modal Retrieval: A Reproducibility Study, [Paper]

• (arXiv 2023.1) Tracr: Compiled Transformers as a Laboratory for Interpretability, [Paper], [Code]

• (arXiv 2023.1) CLIP the Gap: A Single Domain Generalization Approach for Object Detection, [Paper]

• (arXiv 2023.1) Text to Point Cloud Localization with Relation-Enhanced Transformer, [Paper]

• (arXiv 2023.1) GOHSP: A Unified Framework of Graph and Optimization-based Heterogeneous Structured Pruning for Vision Transformer, [Paper]

• (arXiv 2023.1) Toward Building General Foundation Models for Language, Vision, and Vision-Language Understanding Tasks, [Paper]

• (arXiv 2023.1) ViTs for SITS: Vision Transformers for Satellite Image Time Series, [Paper], [Code]

• (arXiv 2023.1) CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP, [Paper]

• (arXiv 2023.1) A Large-Scale Outdoor Multi-modal Dataset and Benchmark for Novel View Synthesis and Implicit Scene Reconstruction, [Paper], [Project]

• (arXiv 2023.1) USER: Unified Semantic Enhancement with Momentum Contrast for Image-Text Retrieval, [Paper], [Code]

• (arXiv 2023.1) SAT: Size-Aware Transformer for 3D Point Cloud Semantic Segmentation, [Paper]

• (arXiv 2023.1) Masked Visual Reconstruction in Language Semantic Space, [Paper], [Code]

• (arXiv 2023.1) Vision Learners Meet Web Image-Text Pairs, [Paper], [Code]

• (arXiv 2023.1) GLIGEN: Open-Set Grounded Text-to-Image Generation, [Paper], [Project]

• (arXiv 2023.1) Learning Customized Visual Models with Retrieval-Augmented Knowledge, [Paper], [Project]

• (arXiv 2023.1) UATVR: Uncertainty-Adaptive Text-Video Retrieval, [Paper]

• (arXiv 2023.1) Learning Aligned Cross-modal Representations for Referring Image Segmentation, [Paper]

• (arXiv 2023.1) T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations, [Paper], [Project]

• (arXiv 2023.1) DSVT: Dynamic Sparse Voxel Transformer with Rotated Sets, [Paper], [Code]

• (arXiv 2023.1) CMAE-V: Contrastive Masked Autoencoders for Video Action Recognition, [Paper]

• (arXiv 2023.1) Generating Templated Caption for Video Grounding, [Paper]

• (arXiv 2023.1) Dyna-DepthFormer: Multi-frame Transformer for Self-Supervised Depth Estimation in Dynamic Scenes, [Paper]

• (arXiv 2023.1) SwinDepth: Unsupervised Depth Estimation using Monocular Sequences via Swin Transformer and Densely Cascaded Network, [Paper]

• (arXiv 2023.1) CLIPTER: Looking at the Bigger Picture in Scene Text Recognition, [Paper]

• (arXiv 2023.1) Temporal Perceiving Video-Language Pre-training, [Paper]

• (arXiv 2023.1) Joint Representation Learning for Text and 3D Point Cloud, [Paper], [Code]

• (arXiv 2023.1) Effective End-to-End Vision Language Pretraining with Semantic Visual Loss, [Paper]

• (arXiv 2023.1) PTA-Det: Point Transformer Associating Point cloud and Image for 3D Object Detection, [Paper]

• (arXiv 2023.1) Face Recognition in the age of CLIP & Billion image datasets, [Paper]

• (arXiv 2023.1) HSTFormer: Hierarchical Spatial-Temporal Transformers for 3D Human Pose Estimation, [Paper], [Code]

• (arXiv 2023.1) Towards Models that Can See and Read, [Paper]

• (arXiv 2023.1) Embodied Agents for Efficient Exploration and Smart Scene Description, [Paper]

• (arXiv 2023.1) Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture, [Paper]

• (arXiv 2023.1) Revisiting the Spatial and Temporal Modeling for Few-shot Action Recognition, [Paper]

• (arXiv 2023.1) Multimodal Video Adapter for Parameter Efficient Video Text Retrieval, [Paper]

• (arXiv 2023.1) Self Supervision Does Not Help Natural Language Supervision at Scale, [Paper]

• (arXiv 2023.1) MULTI-TARGET MULTI-CAMERA VEHICLE TRACKING USING TRANSFORMER-BASED CAMERA LINK MODEL AND SPATIAL-TEMPORAL INFORMATION, [Paper]

• (arXiv 2023.1) ATMAN: Understanding Transformer Predictions Through Memory Efficient Attention Manipulation, [Paper]

• (arXiv 2023.1) DDS: Decoupled Dynamic Scene-Graph Generation Network, [Paper], [Code]

• (arXiv 2023.1) Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences, [Paper]

• (arXiv 2023.1) Image Memorability Prediction with Vision Transformers, [Paper]

• (arXiv 2023.1) HOLISTICALLY EXPLAINABLE VISION TRANSFORMERS, [Paper]

• (arXiv 2023.1) FlatFormer: Flattened Window Attention for Efficient Point Cloud Transformer, [Paper]

• (arXiv 2023.1) LEGO-Net: Learning Regular Rearrangements of Objects in Rooms, [Paper], [Project]

• (arXiv 2023.1) Zorro: the masked multimodal transformer, [Paper]

• (arXiv 2023.1) Towards Robust Video Instance Segmentation with Temporal-Aware Transformer, [Paper]

• (arXiv 2023.1) Learning Open-vocabulary Semantic Segmentation Models From Natural Language Supervision, [Paper], [Project]

• (arXiv 2023.1) Summarize the Past to Predict the Future: Natural Language Descriptions of Context Boost Multimodal Object Interaction Anticipation, [Paper], [Code]

• (arXiv 2023.1) Combined Use of Federated Learning and Image Encryption for Privacy-Preserving Image Classification with Vision Transformer, [Paper]

• (arXiv 2023.1) Slice Transformer and Self-supervised Learning for 6DoF Localization in 3D Point Cloud Maps, [Paper]

• (arXiv 2023.1) IMPROVING ACCURACY OF ZERO-SHOT ACTION RECOGNITION WITH HANDCRAFTED FEATURES, [Paper]

• (arXiv 2023.1) Learning to View: Decision Transformers for Active Object Detection, [Paper]

• (arXiv 2023.1) Visual Semantic Relatedness Dataset for Image Captioning, [Paper], [Code]

• (arXiv 2023.1) VERSATILE NEURAL PROCESSES FOR LEARNING IMPLICIT NEURAL REPRESENTATIONS, [Paper], [Code]

• (arXiv 2023.1) RangeViT: Towards Vision Transformers for 3D Semantic Segmentation in Autonomous Driving, [Paper], [Code]

• (arXiv 2023.1) Exploiting Optical Flow Guidance for Transformer-Based Video Inpainting, [Paper]

• (arXiv 2023.1) Image Super-Resolution using Efficient Striped Window Transformer, [Paper], [Code]

• (arXiv 2023.1) Out of Distribution Performance of State of Art Vision Model, [Paper], [Code]

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• (arXiv 2022.07) AvatarPoser: Articulated Full-Body Pose Tracking from Sparse Motion Sensing, [Paper], [Project]

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• (arXiv 2022.07) Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer, [Paper], [Code]

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• (arXiv 2022.07) Convolutional Embedding Makes Hierarchical Vision Transformer Stronger, [Paper]

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• (arXiv 2022.07) VICTOR: VISUAL INCOMPATIBILITY DETECTION WITH TRANSFORMERS AND FASHION-SPECIFIC CONTRASTIVE PRE-TRAINING, [Paper]

• (arXiv 2022.07) Compositional Human-Scene Interaction Synthesis with Semantic Control, [Paper], [Code]

• (arXiv 2022.07) Static and Dynamic Concepts for Self-supervised Video Representation Learning, [Paper]

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• (arXiv 2022.07) S-Prompts Learning with Pre-trained Transformers: An Occam’s Razor for Domain Incremental Learning, [Paper]

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• (arXiv 2022.07) NewsStories: Illustrating articles with visual summaries, [Paper], [Project]

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