{"total":124,"rows":[{"id":"2203.06604","title":"Masked Autoencoders for Point Cloud Self-supervised Learning","created":"2022-03-13","cited_by_count":459,"area":null},{"id":"2204.12484","title":"ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation","created":"2022-04-26","cited_by_count":456,"area":null},{"id":"2204.03883","title":"Vision Transformers for Single Image Dehazing","created":"2022-04-08","cited_by_count":260,"area":null},{"id":"2206.01191","title":"EfficientFormer: Vision Transformers at MobileNet Speed","created":"2022-06-02","cited_by_count":241,"area":null},{"id":"2312.01597","title":"SCLIP: Rethinking Self-Attention for Dense Vision-Language Inference","created":"2023-12-04","cited_by_count":170,"area":null},{"id":"2205.14953","title":"Multi-Agent Reinforcement Learning is a Sequence Modeling Problem","created":"2022-05-30","cited_by_count":155,"area":null},{"id":"2303.08810","title":"BiFormer: Vision Transformer with Bi-Level Routing Attention","created":"2023-03-15","cited_by_count":151,"area":null},{"id":"2308.03364","title":"Dual Aggregation Transformer for Image Super-Resolution","created":"2023-08-07","cited_by_count":124,"area":null},{"id":"2212.08059","title":"Rethinking Vision Transformers for MobileNet Size and Speed","created":"2022-12-15","cited_by_count":121,"area":null},{"id":"2202.10108","title":"ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyond","created":"2022-02-21","cited_by_count":117,"area":null},{"id":"2304.06906","title":"Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding","created":"2023-04-14","cited_by_count":113,"area":null},{"id":"2211.13654","title":"Cross Aggregation Transformer for Image Restoration","created":"2022-11-24","cited_by_count":92,"area":null},{"id":"2210.01427","title":"Accurate Image Restoration with Attention Retractable Transformer","created":"2022-10-04","cited_by_count":85,"area":null},{"id":"2204.00993","title":"Improving Vision Transformers by Revisiting High-frequency Components","created":"2022-04-03","cited_by_count":76,"area":null},{"id":"2301.08739","title":"FlatFormer: Flattened Window Attention for Efficient Point Cloud Transformer","created":"2023-01-20","cited_by_count":73,"area":null},{"id":"2309.11523","title":"RMT: Retentive Networks Meet Vision Transformers","created":"2023-09-20","cited_by_count":68,"area":null},{"id":"2212.04246","title":"ViTPose++: Vision Transformer for Generic Body Pose Estimation","created":"2022-12-07","cited_by_count":65,"area":null},{"id":"2206.03014","title":"The Devil is in the Labels: Noisy Label Correction for Robust Scene Graph Generation","created":"2022-06-07","cited_by_count":60,"area":null},{"id":"2303.17605","title":"SparseViT: Revisiting Activation Sparsity for Efficient High-Resolution Vision Transformer","created":"2023-03-30","cited_by_count":60,"area":null},{"id":"2411.16061","title":"Scaling Spike-driven Transformer with Efficient Spike Firing Approximation Training","created":"2024-11-25","cited_by_count":59,"area":null},{"id":"2310.11881","title":"A Comparative Study of Image Restoration Networks for General Backbone Network Design","created":"2023-10-18","cited_by_count":57,"area":null},{"id":"2303.16513","title":"Cascaded Local Implicit Transformer for Arbitrary-Scale Super-Resolution","created":"2023-03-29","cited_by_count":52,"area":null},{"id":"2303.15105","title":"Vision Transformer with Quadrangle Attention","created":"2023-03-27","cited_by_count":46,"area":null},{"id":"2211.11167","title":"Vision Transformer with Super Token Sampling","created":"2022-11-21","cited_by_count":44,"area":null},{"id":"2204.08446","title":"VSA: Learning Varied-Size Window Attention in Vision Transformers","created":"2022-04-18","cited_by_count":43,"area":null},{"id":"2407.05878","title":"HiT-SR: Hierarchical Transformer for Efficient Image Super-Resolution","created":"2024-07-08","cited_by_count":42,"area":null},{"id":"2203.10790","title":"ScalableViT: Rethinking the Context-oriented Generalization of Vision Transformer","created":"2022-03-21","cited_by_count":36,"area":null},{"id":"2304.09856","title":"LipsFormer: Introducing Lipschitz Continuity to Vision Transformers","created":"2023-04-19","cited_by_count":34,"area":null},{"id":"2411.07635","title":"Breaking the Low-Rank Dilemma of Linear Attention","created":"2024-11-12","cited_by_count":31,"area":null},{"id":"2504.04701","title":"DFormerv2: Geometry Self-Attention for RGBD Semantic Segmentation","created":"2025-04-07","cited_by_count":31,"area":null},{"id":"2207.10498","title":"Towards Efficient Adversarial Training on Vision Transformers","created":"2022-07-21","cited_by_count":30,"area":null},{"id":"2303.08685","title":"Making Vision Transformers Efficient from A Token Sparsification View","created":"2023-03-15","cited_by_count":29,"area":null},{"id":"2201.12083","title":"DynaMixer: A Vision MLP Architecture with Dynamic Mixing","created":"2022-01-28","cited_by_count":28,"area":null},{"id":"2206.09325","title":"EATFormer: Improving Vision Transformer Inspired by Evolutionary Algorithm","created":"2022-06-19","cited_by_count":27,"area":null},{"id":"2306.00396","title":"Lightweight Vision Transformer with Bidirectional Interaction","created":"2023-06-01","cited_by_count":25,"area":null},{"id":"2502.20087","title":"OverLoCK: An Overview-first-Look-Closely-next ConvNet with Context-Mixing Dynamic Kernels","created":"2025-02-27","cited_by_count":25,"area":null},{"id":"2212.09993","title":"Are Deep Neural Networks SMARTer than Second Graders?","created":"2022-12-20","cited_by_count":24,"area":null},{"id":"2112.04222","title":"Classification-Then-Grounding: Reformulating Video Scene Graphs as Temporal Bipartite Graphs","created":"2021-12-08","cited_by_count":23,"area":null},{"id":"2302.01825","title":"HDFormer: High-order Directed Transformer for 3D Human Pose Estimation","created":"2023-02-03","cited_by_count":23,"area":null},{"id":"2309.01430","title":"DAT++: Spatially Dynamic Vision Transformer with Deformable Attention","created":"2023-09-04","cited_by_count":21,"area":null},{"id":"2310.19380","title":"TransXNet: Learning Both Global and Local Dynamics with a Dual Dynamic Token Mixer for Visual Recognition","created":"2023-10-30","cited_by_count":21,"area":null},{"id":"2408.01120","title":"An Efficient and Effective Transformer Decoder-Based Framework for Multi-Task Visual Grounding","created":"2024-08-02","cited_by_count":21,"area":null},{"id":"2207.13316","title":"NICEST: Noisy Label Correction and Training for Robust Scene Graph Generation","created":"2022-07-27","cited_by_count":19,"area":null},{"id":"2303.06908","title":"CrossFormer++: A Versatile Vision Transformer Hinging on Cross-scale Attention","created":"2023-03-13","cited_by_count":18,"area":null},{"id":"2502.11986","title":"Selective Task Group Updates for Multi-Task Optimization","created":"2025-02-17","cited_by_count":17,"area":null},{"id":"2111.13280","title":"Efficient Self-Ensemble for Semantic Segmentation","created":"2021-11-26","cited_by_count":16,"area":null},{"id":"2203.03937","title":"Dynamic Group Transformer: A General Vision Transformer Backbone with Dynamic Group Attention","created":"2022-03-08","cited_by_count":14,"area":null},{"id":"2308.07770","title":"Multi-scale Promoted Self-adjusting Correlation Learning for Facial Action Unit Detection","created":"2023-08-15","cited_by_count":14,"area":null},{"id":"2407.17261","title":"Embedding-Free Transformer with Inference Spatial Reduction for Efficient Semantic Segmentation","created":"2024-07-24","cited_by_count":14,"area":null},{"id":"2201.13027","title":"BOAT: Bilateral Local Attention Vision Transformer","created":"2022-01-31","cited_by_count":12,"area":null},{"id":"2207.08569","title":"Multi-manifold Attention for Vision Transformers","created":"2022-07-18","cited_by_count":12,"area":null},{"id":"2306.01289","title":"nnMobileNet: Rethinking CNN for Retinopathy Research","created":"2023-06-02","cited_by_count":12,"area":null},{"id":"2311.15157","title":"Advancing Vision Transformers with Group-Mix Attention","created":"2023-11-26","cited_by_count":11,"area":null},{"id":"2503.10259","title":"KVQ: Boosting Video Quality Assessment via Saliency-guided Local Perception","created":"2025-03-13","cited_by_count":11,"area":null},{"id":"2203.01587","title":"Multi-Tailed Vision Transformer for Efficient Inference","created":"2022-03-03","cited_by_count":10,"area":null},{"id":"2308.00574","title":"PVG: Progressive Vision Graph for Vision Recognition","created":"2023-08-01","cited_by_count":10,"area":null},{"id":"2308.05022","title":"Exploring Frequency-Inspired Optimization in Transformer for Efficient Single Image Super-Resolution","created":"2023-08-09","cited_by_count":10,"area":null},{"id":"2207.00784","title":"Learning Cross-Image Object Semantic Relation in Transformer for Few-Shot Fine-Grained Image Classification","created":"2022-07-02","cited_by_count":9,"area":null},{"id":"2212.11115","title":"What Makes for Good Tokenizers in Vision Transformer?","created":"2022-12-21","cited_by_count":9,"area":null},{"id":"2408.14915","title":"Can Transformers Do Enumerative Geometry?","created":"2024-08-27","cited_by_count":8,"area":null},{"id":"2503.16693","title":"ATOM: A Framework of Detecting Query-Based Model Extraction Attacks for Graph Neural Networks","created":"2025-03-20","cited_by_count":8,"area":null},{"id":"2302.02744","title":"AMD-HookNet for Glacier Front Segmentation","created":"2023-02-06","cited_by_count":7,"area":null},{"id":"2302.12416","title":"A Convolutional Vision Transformer for Semantic Segmentation of Side-Scan Sonar Data","created":"2023-02-24","cited_by_count":7,"area":null},{"id":"2401.00722","title":"BRAU-Net++: U-Shaped Hybrid CNN-Transformer Network for Medical Image Segmentation","created":"2024-01-01","cited_by_count":7,"area":null},{"id":"2208.13138","title":"ClusTR: Exploring Efficient Self-attention via Clustering for Vision Transformers","created":"2022-08-28","cited_by_count":6,"area":null},{"id":"2305.11474","title":"Reciprocal Attention Mixing Transformer for Lightweight Image Restoration","created":"2023-05-19","cited_by_count":6,"area":null},{"id":"2309.06282","title":"IBAFormer: Intra-batch Attention Transformer for Domain Generalized Semantic Segmentation","created":"2023-09-12","cited_by_count":6,"area":null},{"id":"2405.13335","title":"Vision Transformer with Sparse Scan Prior","created":"2024-05-22","cited_by_count":6,"area":null},{"id":"2406.10869","title":"Geometric Distortion Guided Transformer for Omnidirectional Image Super-Resolution","created":"2024-06-16","cited_by_count":6,"area":null},{"id":"2204.12143","title":"Deeper Insights into the Robustness of ViTs towards Common Corruptions","created":"2022-04-26","cited_by_count":5,"area":null},{"id":"2312.08614","title":"Factorization Vision Transformer: Modeling Long Range Dependency with Local Window Cost","created":"2023-12-14","cited_by_count":5,"area":null},{"id":"2405.13337","title":"Semantic Equitable Clustering: A Simple and Effective Strategy for Clustering Vision Tokens","created":"2024-05-22","cited_by_count":5,"area":null},{"id":"2407.07441","title":"HAFormer: Unleashing the Power of Hierarchy-Aware Features for Lightweight Semantic Segmentation","created":"2024-07-10","cited_by_count":5,"area":null},{"id":"2211.16368","title":"DBA: Efficient Transformer with Dynamic Bilinear Low-Rank Attention","created":"2022-11-24","cited_by_count":4,"area":null},{"id":"2303.06678","title":"PointPatchMix: Point Cloud Mixing with Patch Scoring","created":"2023-03-12","cited_by_count":4,"area":null},{"id":"2312.10429","title":"ResoNet: Robust and Explainable ENSO Forecasts with Hybrid Convolution and Transformer Networks","created":"2023-12-16","cited_by_count":4,"area":null},{"id":"2403.19882","title":"Enhancing Efficiency in Vision Transformer Networks: Design Techniques and Insights","created":"2024-03-28","cited_by_count":4,"area":null},{"id":"2410.08582","title":"DeBiFormer: Vision Transformer with Deformable Agent Bi-level Routing Attention","created":"2024-10-11","cited_by_count":4,"area":null},{"id":"2410.11428","title":"CTA-Net: A CNN-Transformer Aggregation Network for Improving Multi-Scale Feature Extraction","created":"2024-10-15","cited_by_count":4,"area":null},{"id":"2411.07814","title":"Community Research Earth Digital Intelligence Twin (CREDIT)","created":"2024-11-09","cited_by_count":4,"area":null},{"id":"2507.07485","title":"Resolving Token-Space Gradient Conflicts: Token Space Manipulation for Transformer-Based Multi-Task Learning","created":"2025-07-10","cited_by_count":4,"area":null},{"id":"2301.11513","title":"CellMix: A General Instance Relationship based Method for Data Augmentation Towards Pathology Image Classification","created":"2023-01-27","cited_by_count":3,"area":null},{"id":"2308.05872","title":"Vision Backbone Enhancement via Multi-Stage Cross-Scale Attention","created":"2023-08-10","cited_by_count":3,"area":null},{"id":"2310.15672","title":"How Much Context Does My Attention-Based ASR System Need?","created":"2023-10-24","cited_by_count":3,"area":null},{"id":"2207.02126","title":"Improving Semantic Segmentation in Transformers using Hierarchical Inter-Level Attention","created":"2022-07-05","cited_by_count":2,"area":null},{"id":"2307.07812","title":"Multiscale Memory Comparator Transformer for Few-Shot Video Segmentation","created":"2023-07-15","cited_by_count":2,"area":null},{"id":"2312.00412","title":"SCHEME: Scalable Channel Mixer for Vision Transformers","created":"2023-12-01","cited_by_count":2,"area":null},{"id":"2403.04200","title":"ACC-ViT : Atrous Convolution's Comeback in Vision Transformers","created":"2024-03-07","cited_by_count":2,"area":null},{"id":"2406.08859","title":"Fusion of regional and sparse attention in Vision Transformers","created":"2024-06-13","cited_by_count":2,"area":null},{"id":"2407.13920","title":"DuoFormer: Leveraging Hierarchical Visual Representations by Local and Global Attention","created":"2024-07-18","cited_by_count":2,"area":null},{"id":"2211.14255","title":"Degenerate Swin to Win: Plain Window-based Transformer without Sophisticated Operations","created":"2022-11-25","cited_by_count":1,"area":null},{"id":"2212.05677","title":"Masked autoencoders are effective solution to transformer data-hungry","created":"2022-12-12","cited_by_count":1,"area":null},{"id":"2304.05930","title":"MED-VT++: Unifying Multimodal Learning with a Multiscale Encoder-Decoder Video Transformer","created":"2023-04-12","cited_by_count":1,"area":null},{"id":"2306.03373","title":"CiT-Net: Convolutional Neural Networks Hand in Hand with Vision Transformers for Medical Image Segmentation","created":"2023-06-06","cited_by_count":1,"area":null},{"id":"2306.04086","title":"TEC-Net: Vision Transformer Embrace Convolutional Neural Networks for Medical Image Segmentation","created":"2023-06-07","cited_by_count":1,"area":null},{"id":"2306.16903","title":"Leveraging Cross-Utterance Context For ASR Decoding","created":"2023-06-29","cited_by_count":1,"area":null},{"id":"2309.12639","title":"CINFormer: Transformer network with multi-stage CNN feature injection for surface defect segmentation","created":"2023-09-22","cited_by_count":1,"area":null},{"id":"2310.06629","title":"EViT: An Eagle Vision Transformer with Bi-Fovea Self-Attention","created":"2023-10-10","cited_by_count":1,"area":null},{"id":"2311.13502","title":"Bitformer: An efficient Transformer with bitwise operation-based attention for Big Data Analytics at low-cost low-precision devices","created":"2023-11-22","cited_by_count":1,"area":null},{"id":"2404.17854","title":"GLIMS: Attention-Guided Lightweight Multi-Scale Hybrid Network for Volumetric Semantic Segmentation","created":"2024-04-27","cited_by_count":1,"area":null}],"next_cursor":"100"}