{"list":"other","total":56,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"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":"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":"1709.01507","title":"Squeeze-and-Excitation Networks","created":"2017-09-05","cited_by_count":4789},{"id":"1611.01144","title":"Categorical Reparameterization with Gumbel-Softmax","created":"2016-11-03","cited_by_count":2739},{"id":"1609.04802","title":"Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network","created":"2016-09-15","cited_by_count":1908},{"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":"1707.02921","title":"Enhanced Deep Residual Networks for Single Image Super-Resolution","created":"2017-07-10","cited_by_count":1552},{"id":"1807.02758","title":"Image Super-Resolution Using Very Deep Residual Channel Attention Networks","created":"2018-07-08","cited_by_count":1264},{"id":"1501.00092","title":"Image Super-Resolution Using Deep Convolutional Networks","created":"2014-12-31","cited_by_count":1224},{"id":"1511.04587","title":"Accurate Image Super-Resolution Using Very Deep Convolutional Networks","created":"2015-11-14","cited_by_count":903},{"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":"1802.08797","title":"Residual Dense Network for Image Super-Resolution","created":"2018-02-24","cited_by_count":799},{"id":"1811.11168","title":"Deformable ConvNets v2: More Deformable, Better Results","created":"2018-11-27","cited_by_count":670},{"id":"1909.11065","title":"Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation","created":"2019-09-24","cited_by_count":576},{"id":"1602.07868","title":"Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks","created":"2016-02-25","cited_by_count":543},{"id":"2205.04437","title":"Activating More Pixels in Image Super-Resolution Transformer","created":"2022-05-09","cited_by_count":438},{"id":"1904.00523","title":"Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model","created":"2019-04-01","cited_by_count":403},{"id":"1904.11486","title":"Making Convolutional Networks Shift-Invariant Again","created":"2019-04-25","cited_by_count":391},{"id":"1912.03458","title":"Dynamic Convolution: Attention over Convolution Kernels","created":"2019-12-07","cited_by_count":356},{"id":"1510.04389","title":"Sketch-based Manga Retrieval using Manga109 Dataset","created":"2015-10-15","cited_by_count":329},{"id":"1605.09673","title":"Dynamic Filter Networks","created":"2016-05-31","cited_by_count":319},{"id":"1812.03411","title":"Feature Denoising for Improving Adversarial Robustness","created":"2018-12-09","cited_by_count":314},{"id":"1803.08664","title":"Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network","created":"2018-03-23","cited_by_count":264},{"id":"2008.01928","title":"Component Divide-and-Conquer for Real-World Image Super-Resolution","created":"2020-08-05","cited_by_count":246},{"id":"1909.11856","title":"Lightweight Image Super-Resolution with Information Multi-distillation Network","created":"2019-09-26","cited_by_count":227},{"id":"1803.02735","title":"Deep Back-Projection Networks For Super-Resolution","created":"2018-03-07","cited_by_count":225},{"id":"1901.10430","title":"Pay Less Attention with Lightweight and Dynamic Convolutions","created":"2019-01-29","cited_by_count":211},{"id":"1909.02701","title":"Visual Semantic Reasoning for Image-Text Matching","created":"2019-09-06","cited_by_count":185},{"id":"2009.11551","title":"Residual Feature Distillation Network for Lightweight Image Super-Resolution","created":"2020-09-24","cited_by_count":122},{"id":"1803.09454","title":"Fast and Accurate Single Image Super-Resolution via Information Distillation Network","created":"2018-03-26","cited_by_count":121},{"id":"2105.10422","title":"LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-Resolution and Beyond","created":"2021-05-21","cited_by_count":88},{"id":"2010.01073","title":"Efficient Image Super-Resolution Using Pixel Attention","created":"2020-10-02","cited_by_count":87},{"id":"2104.06174","title":"Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and Baseline","created":"2021-04-13","cited_by_count":61},{"id":"1511.02228","title":"Seven ways to improve example-based single image super resolution","created":"2015-11-06","cited_by_count":51},{"id":"1809.11130","title":"Channel-wise and Spatial Feature Modulation Network for Single Image Super-Resolution","created":"2018-09-28","cited_by_count":42},{"id":"1803.11189","title":"Iterative Visual Reasoning Beyond Convolutions","created":"2018-03-29","cited_by_count":38},{"id":"2107.12541","title":"BridgeNet: A Joint Learning Network of Depth Map Super-Resolution and Monocular Depth Estimation","created":"2021-07-27","cited_by_count":31},{"id":"2203.09642","title":"Cascade Transformers for End-to-End Person Search","created":"2022-03-17","cited_by_count":31},{"id":"2203.13963","title":"Transformer-empowered Multi-scale Contextual Matching and Aggregation for Multi-contrast MRI Super-resolution","created":"2022-03-26","cited_by_count":30},{"id":"2112.08655","title":"Feature Distillation Interaction Weighting Network for Lightweight Image Super-Resolution","created":"2021-12-16","cited_by_count":29},{"id":"1901.01074","title":"Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search","created":"2019-01-04","cited_by_count":24},{"id":"1910.05577","title":"Context-Gated Convolution","created":"2019-10-12","cited_by_count":23},{"id":"2008.12912","title":"Multi-Attention Based Ultra Lightweight Image Super-Resolution","created":"2020-08-29","cited_by_count":23},{"id":"2204.13286","title":"Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric CNN and Recursive Transformer","created":"2022-04-28","cited_by_count":22},{"id":"2104.10488","title":"A Two-Stage Attentive Network for Single Image Super-Resolution","created":"2021-04-21","cited_by_count":20},{"id":"2008.13084","title":"MDCN: Multi-scale Dense Cross Network for Image Super-Resolution","created":"2020-08-30","cited_by_count":19},{"id":"2112.04488","title":"A Dynamic Residual Self-Attention Network for Lightweight Single Image Super-Resolution","created":"2021-12-08","cited_by_count":19},{"id":"2204.08696","title":"CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution","created":"2022-04-19","cited_by_count":19},{"id":"1904.02358","title":"Lightweight Image Super-Resolution with Adaptive Weighted Learning Network","created":"2019-04-04","cited_by_count":16},{"id":"1802.10419","title":"Convolutional Neural Networks with Alternately Updated Clique","created":"2018-02-28","cited_by_count":14},{"id":"2103.13028","title":"Lightweight Image Super-Resolution with Multi-scale Feature Interaction Network","created":"2021-03-24","cited_by_count":9},{"id":"2007.05835","title":"Lightweight Modules for Efficient Deep Learning based Image Restoration","created":"2020-07-11","cited_by_count":6}],"next_cursor":null}