{"list":"other","total":58,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"2010.11929","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","created":"2020-10-22","cited_by_count":30186},{"id":"1505.04597","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","created":"2015-05-18","cited_by_count":19713},{"id":"2103.14030","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","created":"2021-03-25","cited_by_count":12139},{"id":"1801.03924","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","created":"2018-01-11","cited_by_count":11177},{"id":"2304.02643","title":"Segment Anything","created":"2023-04-05","cited_by_count":8234},{"id":"2005.12872","title":"End-to-End Object Detection with Transformers","created":"2020-05-26","cited_by_count":7414},{"id":"2012.12877","title":"Training data-efficient image transformers & distillation through attention","created":"2020-12-23","cited_by_count":4586},{"id":"2105.15203","title":"SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers","created":"2021-05-31","cited_by_count":2940},{"id":"1603.08155","title":"Perceptual Losses for Real-Time Style Transfer and Super-Resolution","created":"2016-03-27","cited_by_count":2694},{"id":"1606.04797","title":"V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation","created":"2016-06-15","cited_by_count":2292},{"id":"2102.12122","title":"Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions","created":"2021-02-24","cited_by_count":1962},{"id":"2108.10257","title":"SwinIR: Image Restoration Using Swin Transformer","created":"2021-08-23","cited_by_count":1887},{"id":"2111.09881","title":"Restormer: Efficient Transformer for High-Resolution Image Restoration","created":"2021-11-18","cited_by_count":1627},{"id":"1904.10509","title":"Generating Long Sequences with Sparse Transformers","created":"2019-04-23","cited_by_count":1546},{"id":"2012.15840","title":"Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers","created":"2020-12-31","cited_by_count":1369},{"id":"2101.11986","title":"Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet","created":"2021-01-28","cited_by_count":1101},{"id":"2101.01169","title":"Transformers in Vision: A Survey","created":"2021-01-04","cited_by_count":1013},{"id":"2012.00364","title":"Pre-Trained Image Processing Transformer","created":"2020-12-01","cited_by_count":893},{"id":"2106.03106","title":"Uformer: A General U-Shaped Transformer for Image Restoration","created":"2021-06-06","cited_by_count":866},{"id":"2004.07728","title":"Image Quality Assessment: Unifying Structure and Texture Similarity","created":"2020-04-16","cited_by_count":811},{"id":"2012.12556","title":"A Survey on Visual Transformer","created":"2020-12-23","cited_by_count":654},{"id":"1506.01186","title":"Cyclical Learning Rates for Training Neural Networks","created":"2015-06-03","cited_by_count":629},{"id":"2104.13840","title":"Twins: Revisiting the Design of Spatial Attention in Vision Transformers","created":"2021-04-28","cited_by_count":565},{"id":"1804.02815","title":"Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform","created":"2018-04-09","cited_by_count":371},{"id":"2201.02973","title":"MAXIM: Multi-Axis MLP for Image Processing","created":"2022-01-09","cited_by_count":302},{"id":"2003.10985","title":"Multi-Scale Progressive Fusion Network for Single Image Deraining","created":"2020-03-24","cited_by_count":260},{"id":"2002.11296","title":"Sparse Sinkhorn Attention","created":"2020-02-26","cited_by_count":251},{"id":"2105.06086","title":"HINet: Half Instance Normalization Network for Image Restoration","created":"2021-05-13","cited_by_count":237},{"id":"2303.11950","title":"Learning A Sparse Transformer Network for Effective Image Deraining","created":"2023-03-21","cited_by_count":205},{"id":"1701.05957","title":"Image De-raining Using a Conditional Generative Adversarial Network","created":"2017-01-21","cited_by_count":197},{"id":"2112.06375","title":"Embracing Single Stride 3D Object Detector with Sparse Transformer","created":"2021-12-13","cited_by_count":193},{"id":"2212.11548","title":"Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method","created":"2022-12-22","cited_by_count":174},{"id":"1909.00015","title":"Adaptively Sparse Transformers","created":"2019-08-30","cited_by_count":155},{"id":"2111.12763","title":"Sparse is Enough in Scaling Transformers","created":"2021-11-24","cited_by_count":86},{"id":"2210.01427","title":"Accurate Image Restoration with Attention Retractable Transformer","created":"2022-10-04","cited_by_count":85},{"id":"2308.14036","title":"MB-TaylorFormer: Multi-branch Efficient Transformer Expanded by Taylor Formula for Image Dehazing","created":"2023-08-27","cited_by_count":81},{"id":"2106.00515","title":"KVT: k-NN Attention for Boosting Vision Transformers","created":"2021-05-28","cited_by_count":65},{"id":"2107.05768","title":"Combiner: Full Attention Transformer with Sparse Computation Cost","created":"2021-07-12","cited_by_count":64},{"id":"1912.11637","title":"Explicit Sparse Transformer: Concentrated Attention Through Explicit Selection","created":"2019-12-25","cited_by_count":62},{"id":"2204.09331","title":"NFormer: Robust Person Re-identification with Neighbor Transformer","created":"2022-04-20","cited_by_count":57},{"id":"2003.04857","title":"Image Restoration for Under-Display Camera","created":"2020-03-10","cited_by_count":47},{"id":"2308.14153","title":"Sparse Sampling Transformer with Uncertainty-Driven Ranking for Unified Removal of Raindrops and Rain Streaks","created":"2023-08-27","cited_by_count":45},{"id":"2201.01893","title":"Flow-Guided Sparse Transformer for Video Deblurring","created":"2022-01-06","cited_by_count":43},{"id":"2104.09556","title":"Removing Diffraction Image Artifacts in Under-Display Camera via Dynamic Skip Connection Network","created":"2021-04-19","cited_by_count":31},{"id":"2207.06124","title":"DynaST: Dynamic Sparse Transformer for Exemplar-Guided Image Generation","created":"2022-07-13","cited_by_count":24},{"id":"2105.03830","title":"Beyond Monocular Deraining: Parallel Stereo Deraining Network Via Semantic Prior","created":"2021-05-09","cited_by_count":18},{"id":"2304.06019","title":"Generating Aligned Pseudo-Supervision from Non-Aligned Data for Image Restoration in Under-Display Camera","created":"2023-04-12","cited_by_count":16},{"id":"2008.07742","title":"UDC 2020 Challenge on Image Restoration of Under-Display Camera: Methods and Results","created":"2020-08-18","cited_by_count":15},{"id":"2209.07052","title":"MIPI 2022 Challenge on Under-Display Camera Image Restoration: Methods and Results","created":"2022-09-15","cited_by_count":14},{"id":"2308.04163","title":"Under-Display Camera Image Restoration with Scattering Effect","created":"2023-08-08","cited_by_count":12},{"id":"2009.09393","title":"Transform Domain Pyramidal Dilated Convolution Networks For Restoration of Under Display Camera Images","created":"2020-09-20","cited_by_count":11},{"id":"2209.01809","title":"UDC-UNet: Under-Display Camera Image Restoration via U-Shape Dynamic Network","created":"2022-09-05","cited_by_count":11},{"id":"2008.06229","title":"Deep Atrous Guided Filter for Image Restoration in Under Display Cameras","created":"2020-08-14","cited_by_count":10},{"id":"2308.10196","title":"Blind Face Restoration for Under-Display Camera via Dictionary Guided Transformer","created":"2023-08-20","cited_by_count":5},{"id":"2111.01511","title":"ISP-Agnostic Image Reconstruction for Under-Display Cameras","created":"2021-11-02","cited_by_count":3}],"next_cursor":null}