{"total":39,"rows":[{"id":"2502.12524","title":"YOLOv12: Attention-Centric Real-Time Object Detectors","created":"2025-02-18","cited_by_count":305,"area":null},{"id":"2204.03883","title":"Vision Transformers for Single Image Dehazing","created":"2022-04-08","cited_by_count":260,"area":null},{"id":"2204.03645","title":"DaViT: Dual Attention Vision Transformers","created":"2022-04-07","cited_by_count":175,"area":null},{"id":"2112.11010","title":"MPViT: Multi-Path Vision Transformer for Dense Prediction","created":"2021-12-21","cited_by_count":122,"area":null},{"id":"2111.01236","title":"Multi-Scale High-Resolution Vision Transformer for Semantic Segmentation","created":"2021-11-01","cited_by_count":92,"area":null},{"id":"2112.13906","title":"Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?","created":"2021-12-27","cited_by_count":85,"area":null},{"id":"2210.01427","title":"Accurate Image Restoration with Attention Retractable Transformer","created":"2022-10-04","cited_by_count":85,"area":null},{"id":"2111.09833","title":"TransMix: Attend to Mix for Vision Transformers","created":"2021-11-18","cited_by_count":79,"area":null},{"id":"2203.03821","title":"CF-ViT: A General Coarse-to-Fine Method for Vision Transformer","created":"2022-03-08","cited_by_count":63,"area":null},{"id":"2210.01820","title":"MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models","created":"2022-10-04","cited_by_count":47,"area":null},{"id":"2207.01223","title":"A Survey on Label-efficient Deep Image Segmentation: Bridging the Gap between Weak Supervision and Dense Prediction","created":"2022-07-04","cited_by_count":46,"area":null},{"id":"2211.11436","title":"N-Gram in Swin Transformers for Efficient Lightweight Image Super-Resolution","created":"2022-11-21","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":"2109.05422","title":"Sparse MLP for Image Recognition: Is Self-Attention Really Necessary?","created":"2021-09-12","cited_by_count":38,"area":null},{"id":"2302.01791","title":"DilateFormer: Multi-Scale Dilated Transformer for Visual Recognition","created":"2023-02-03","cited_by_count":34,"area":null},{"id":"2109.03810","title":"Scaled ReLU Matters for Training Vision Transformers","created":"2021-09-08","cited_by_count":29,"area":null},{"id":"2204.13154","title":"Attention Mechanism in Neural Networks: Where it Comes and Where it Goes","created":"2022-04-27","cited_by_count":29,"area":null},{"id":"2211.12735","title":"Fast-iTPN: Integrally Pre-Trained Transformer Pyramid Network with Token Migration","created":"2022-11-23","cited_by_count":24,"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":"2404.16573","title":"Multi-Scale Representations by Varying Window Attention for Semantic Segmentation","created":"2024-04-25","cited_by_count":20,"area":null},{"id":"2112.12786","title":"ELSA: Enhanced Local Self-Attention for Vision Transformer","created":"2021-12-23","cited_by_count":18,"area":null},{"id":"2304.00212","title":"Devil is in the Queries: Advancing Mask Transformers for Real-world Medical Image Segmentation and Out-of-Distribution Localization","created":"2023-04-01","cited_by_count":16,"area":null},{"id":"2203.14313","title":"Beyond Masking: Demystifying Token-Based Pre-Training for Vision Transformers","created":"2022-03-27","cited_by_count":14,"area":null},{"id":"2308.13998","title":"Computation-efficient Deep Learning for Computer Vision: A Survey","created":"2023-08-27","cited_by_count":14,"area":null},{"id":"2207.04044","title":"kMaX-DeepLab: k-means Mask Transformer","created":"2022-07-08","cited_by_count":12,"area":null},{"id":"2301.11553","title":"Robust Transformer with Locality Inductive Bias and Feature Normalization","created":"2023-01-27","cited_by_count":12,"area":null},{"id":"2304.03282","title":"Visual Dependency Transformers: Dependency Tree Emerges from Reversed Attention","created":"2023-04-06","cited_by_count":12,"area":null},{"id":"2204.07366","title":"ResT V2: Simpler, Faster and Stronger","created":"2022-04-15","cited_by_count":9,"area":null},{"id":"2207.13050","title":"Efficient High-Resolution Deep Learning: A Survey","created":"2022-07-26","cited_by_count":9,"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":"2402.13578","title":"TransGOP: Transformer-Based Gaze Object Prediction","created":"2024-02-21","cited_by_count":6,"area":null},{"id":"2301.01772","title":"Infomaxformer: Maximum Entropy Transformer for Long Time-Series Forecasting Problem","created":"2023-01-04","cited_by_count":5,"area":null},{"id":"2304.03481","title":"PSLT: A Light-weight Vision Transformer with Ladder Self-Attention and Progressive Shift","created":"2023-04-07","cited_by_count":5,"area":null},{"id":"2308.01146","title":"UCDFormer: Unsupervised Change Detection Using a Transformer-driven Image Translation","created":"2023-08-02","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":"2308.05872","title":"Vision Backbone Enhancement via Multi-Stage Cross-Scale Attention","created":"2023-08-10","cited_by_count":3,"area":null},{"id":"2401.14729","title":"Sketch and Refine: Towards Fast and Accurate Lane Detection","created":"2024-01-26","cited_by_count":3,"area":null},{"id":"2306.03679","title":"Human-imperceptible, Machine-recognizable Images","created":"2023-06-06","cited_by_count":1,"area":null},{"id":"2604.06783","title":"Insights from Visual Cognition: Understanding Human Action Dynamics with Overall Glance and Refined Gaze Transformer","created":"2026-04-08","cited_by_count":0,"area":null}],"next_cursor":null}