{"list":"other","total":66,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1505.04597","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","created":"2015-05-18","cited_by_count":19713},{"id":"1409.1556","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","created":"2014-09-04","cited_by_count":15364},{"id":"2103.14030","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","created":"2021-03-25","cited_by_count":12139},{"id":"2005.12872","title":"End-to-End Object Detection with Transformers","created":"2020-05-26","cited_by_count":7414},{"id":"1512.00567","title":"Rethinking the Inception Architecture for Computer Vision","created":"2015-12-02","cited_by_count":5475},{"id":"1608.03983","title":"SGDR: Stochastic Gradient Descent with Warm Restarts","created":"2016-08-13","cited_by_count":5045},{"id":"2012.12877","title":"Training data-efficient image transformers & distillation through attention","created":"2020-12-23","cited_by_count":4586},{"id":"1409.4842","title":"Going Deeper with Convolutions","created":"2014-09-17","cited_by_count":4241},{"id":"1704.04861","title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications","created":"2017-04-17","cited_by_count":3845},{"id":"1610.02357","title":"Xception: Deep Learning with Depthwise Separable Convolutions","created":"2016-10-07","cited_by_count":2332},{"id":"2006.16236","title":"Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention","created":"2020-06-29","cited_by_count":2022},{"id":"1905.02244","title":"Searching for MobileNetV3","created":"2019-05-06","cited_by_count":1994},{"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":"2009.14794","title":"Rethinking Attention with Performers","created":"2020-09-30","cited_by_count":1601},{"id":"1703.06211","title":"Deformable Convolutional Networks","created":"2017-03-17","cited_by_count":1488},{"id":"2006.04768","title":"Linformer: Self-Attention with Linear Complexity","created":"2020-06-08","cited_by_count":1474},{"id":"1609.05158","title":"Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network","created":"2016-09-16","cited_by_count":1204},{"id":"1511.07122","title":"Multi-Scale Context Aggregation by Dilated Convolutions","created":"2015-11-23","cited_by_count":1133},{"id":"2106.03106","title":"Uformer: A General U-Shaped Transformer for Image Restoration","created":"2021-06-06","cited_by_count":866},{"id":"2103.14899","title":"CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification","created":"2021-03-27","cited_by_count":806},{"id":"2106.13797","title":"PVT v2: Improved Baselines with Pyramid Vision Transformer","created":"2021-06-25","cited_by_count":751},{"id":"1906.05909","title":"Stand-Alone Self-Attention in Vision Models","created":"2019-06-13","cited_by_count":542},{"id":"2107.00652","title":"CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows","created":"2021-07-01","cited_by_count":527},{"id":"2204.01697","title":"MaxViT: Multi-Axis Vision Transformer","created":"2022-04-04","cited_by_count":476},{"id":"1712.04143","title":"Benchmarking Single Image Dehazing and Beyond","created":"2017-12-12","cited_by_count":447},{"id":"1812.01243","title":"Efficient Attention: Attention with Linear Complexities","created":"2018-12-04","cited_by_count":368},{"id":"1601.07661","title":"DehazeNet: An End-to-End System for Single Image Haze Removal","created":"2016-01-28","cited_by_count":314},{"id":"2003.06792","title":"Learning Enriched Features for Real Image Restoration and Enhancement","created":"2020-03-15","cited_by_count":312},{"id":"1911.07559","title":"FFA-Net: Feature Fusion Attention Network for Single Image Dehazing","created":"2019-11-18","cited_by_count":307},{"id":"2202.06709","title":"How Do Vision Transformers Work?","created":"2022-02-14","cited_by_count":307},{"id":"2201.02973","title":"MAXIM: Multi-Axis MLP for Image Processing","created":"2022-01-09","cited_by_count":302},{"id":"2204.03883","title":"Vision Transformers for Single Image Dehazing","created":"2022-04-08","cited_by_count":260},{"id":"2004.13388","title":"Multi-Scale Boosted Dehazing Network with Dense Feature Fusion","created":"2020-04-28","cited_by_count":230},{"id":"1908.03245","title":"GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing","created":"2019-08-08","cited_by_count":227},{"id":"2104.06399","title":"Co-Scale Conv-Attentional Image Transformers","created":"2021-04-13","cited_by_count":224},{"id":"2104.09367","title":"Contrastive Learning for Compact Single Image Dehazing","created":"2021-04-19","cited_by_count":219},{"id":"2202.08791","title":"cosFormer: Rethinking Softmax in Attention","created":"2022-02-17","cited_by_count":213},{"id":"1904.11491","title":"Local Relation Networks for Image Recognition","created":"2019-04-25","cited_by_count":210},{"id":"1807.05698","title":"Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining","created":"2018-07-16","cited_by_count":205},{"id":"1708.04512","title":"DesnowNet: Context-Aware Deep Network for Snow Removal","created":"2017-08-15","cited_by_count":200},{"id":"2004.01860","title":"Deblurring by Realistic Blurring","created":"2020-04-04","cited_by_count":188},{"id":"1803.08396","title":"Densely Connected Pyramid Dehazing Network","created":"2018-03-22","cited_by_count":145},{"id":"1804.00213","title":"Gated Fusion Network for Single Image Dehazing","created":"2018-03-31","cited_by_count":133},{"id":"2112.11010","title":"MPViT: Multi-Path Vision Transformer for Dense Prediction","created":"2021-12-21","cited_by_count":122},{"id":"1904.02904","title":"Dense Haze: A benchmark for image dehazing with dense-haze and haze-free images","created":"2019-04-05","cited_by_count":107},{"id":"2205.12956","title":"Inception Transformer","created":"2022-05-25","cited_by_count":99},{"id":"1804.05101","title":"O-HAZE: a dehazing benchmark with real hazy and haze-free outdoor images","created":"2018-04-13","cited_by_count":95},{"id":"2201.10700","title":"Deep Image Deblurring: A Survey","created":"2022-01-26","cited_by_count":94},{"id":"2103.11298","title":"Deep Dense Multi-scale Network for Snow Removal Using Semantic and Geometric Priors","created":"2021-03-21","cited_by_count":82},{"id":"1805.05308","title":"Cycle-Dehaze: Enhanced CycleGAN for Single Image Dehazing","created":"2018-05-14","cited_by_count":79},{"id":"2308.03033","title":"FourLLIE: Boosting Low-Light Image Enhancement by Fourier Frequency Information","created":"2023-08-06","cited_by_count":64},{"id":"2305.17863","title":"GridFormer: Residual Dense Transformer with Grid Structure for Image Restoration in Adverse Weather Conditions","created":"2023-05-29","cited_by_count":62},{"id":"2106.01439","title":"NTIRE 2021 Challenge on High Dynamic Range Imaging: Dataset, Methods and Results","created":"2021-06-02","cited_by_count":36},{"id":"2103.12318","title":"Enhanced Spatio-Temporal Interaction Learning for Video Deraining: A Faster and Better Framework","created":"2021-03-23","cited_by_count":30},{"id":"2105.03830","title":"Beyond Monocular Deraining: Parallel Stereo Deraining Network Via Semantic Prior","created":"2021-05-09","cited_by_count":18},{"id":"2004.13824","title":"Pyramid Attention Networks for Image Restoration","created":"2020-04-28","cited_by_count":17},{"id":"2212.11473","title":"Restoring Vision in Hazy Weather with Hierarchical Contrastive Learning","created":"2022-12-22","cited_by_count":17},{"id":"2207.04754","title":"Snow Mask Guided Adaptive Residual Network for Image Snow Removal","created":"2022-07-11","cited_by_count":13},{"id":"2109.14382","title":"UFO-ViT: High Performance Linear Vision Transformer without Softmax","created":"2021-09-29","cited_by_count":11},{"id":"2112.02279","title":"U2-Former: A Nested U-shaped Transformer for Image Restoration","created":"2021-12-04","cited_by_count":10},{"id":"2106.02809","title":"T-Net: Deep Stacked Scale-Iteration Network for Image Dehazing","created":"2021-06-05","cited_by_count":8},{"id":"2109.07100","title":"Complementary Feature Enhanced Network with Vision Transformer for Image Dehazing","created":"2021-09-15","cited_by_count":8},{"id":"2308.03029","title":"Brighten-and-Colorize: A Decoupled Network for Customized Low-Light Image Enhancement","created":"2023-08-06","cited_by_count":3}],"next_cursor":null}