{"list":"other","total":69,"rows":[{"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":"1912.01703","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","created":"2019-12-03","cited_by_count":20305},{"id":"1405.0312","title":"Microsoft COCO: Common Objects in Context","created":"2014-05-01","cited_by_count":17608},{"id":"1409.1556","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","created":"2014-09-04","cited_by_count":15364},{"id":"1411.4038","title":"Fully Convolutional Networks for Semantic Segmentation","created":"2014-11-14","cited_by_count":4859},{"id":"1604.01685","title":"The Cityscapes Dataset for Semantic Urban Scene Understanding","created":"2016-04-06","cited_by_count":4444},{"id":"1606.00915","title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","created":"2016-06-02","cited_by_count":3241},{"id":"1802.02611","title":"Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation","created":"2018-02-07","cited_by_count":3223},{"id":"2105.15203","title":"SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers","created":"2021-05-31","cited_by_count":2940},{"id":"1612.01105","title":"Pyramid Scene Parsing Network","created":"2016-12-04","cited_by_count":2479},{"id":"1409.7495","title":"Unsupervised Domain Adaptation by Backpropagation","created":"2014-09-26","cited_by_count":1917},{"id":"1711.03213","title":"CyCADA: Cycle-Consistent Adversarial Domain Adaptation","created":"2017-11-08","cited_by_count":1082},{"id":"1608.02192","title":"Playing for Data: Ground Truth from Computer Games","created":"2016-08-07","cited_by_count":818},{"id":"1811.12833","title":"ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation","created":"2018-11-30","cited_by_count":623},{"id":"1802.10349","title":"Learning to Adapt Structured Output Space for Semantic Segmentation","created":"2018-02-28","cited_by_count":584},{"id":"2004.05498","title":"FDA: Fourier Domain Adaptation for Semantic Segmentation","created":"2020-04-11","cited_by_count":541},{"id":"1703.01780","title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","created":"2017-03-06","cited_by_count":472},{"id":"1908.09822","title":"Confidence Regularized Self-Training","created":"2019-08-26","cited_by_count":419},{"id":"1904.10620","title":"Bidirectional Learning for Domain Adaptation of Semantic Segmentation","created":"2019-04-24","cited_by_count":308},{"id":"1809.09478","title":"Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation","created":"2018-09-25","cited_by_count":295},{"id":"1901.02985","title":"Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation","created":"2019-01-10","cited_by_count":260},{"id":"1912.01805","title":"Adversarial Domain Adaptation with Domain Mixup","created":"2019-12-04","cited_by_count":250},{"id":"1612.02649","title":"FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation","created":"2016-12-08","cited_by_count":243},{"id":"2007.08702","title":"DACS: Domain Adaptation via Cross-domain Mixed Sampling","created":"2020-07-17","cited_by_count":226},{"id":"2007.07936","title":"ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning","created":"2020-07-15","cited_by_count":209},{"id":"2004.07703","title":"Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision","created":"2020-04-16","cited_by_count":198},{"id":"2008.12197","title":"Instance Adaptive Self-Training for Unsupervised Domain Adaptation","created":"2020-08-27","cited_by_count":177},{"id":"2003.00867","title":"Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation","created":"2020-03-02","cited_by_count":153},{"id":"1901.05427","title":"Domain Adaptation for Structured Output via Discriminative Patch Representations","created":"2019-01-16","cited_by_count":150},{"id":"2105.00097","title":"Self-supervised Augmentation Consistency for Adapting Semantic Segmentation","created":"2021-04-30","cited_by_count":149},{"id":"2007.09222","title":"Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation","created":"2020-07-17","cited_by_count":142},{"id":"1909.13589","title":"Domain Adaptation for Semantic Segmentation with Maximum Squares Loss","created":"2019-09-30","cited_by_count":138},{"id":"1909.00589","title":"Self-Ensembling with GAN-based Data Augmentation for Domain Adaptation in Semantic Segmentation","created":"2019-09-02","cited_by_count":126},{"id":"2003.08040","title":"Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation","created":"2020-03-18","cited_by_count":126},{"id":"1812.05040","title":"Learning Semantic Segmentation from Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach","created":"2018-12-12","cited_by_count":119},{"id":"1704.08509","title":"No More Discrimination: Cross City Adaptation of Road Scene Segmenters","created":"2017-04-27","cited_by_count":111},{"id":"2001.03182","title":"CrDoCo: Pixel-level Domain Transfer with Cross-Domain Consistency","created":"2020-01-09","cited_by_count":107},{"id":"1903.12212","title":"All about Structure: Adapting Structural Information across Domains for Boosting Semantic Segmentation","created":"2019-03-26","cited_by_count":104},{"id":"2008.06374","title":"PointMixup: Augmentation for Point Clouds","created":"2020-08-14","cited_by_count":104},{"id":"2007.03141","title":"Dual Mixup Regularized Learning for Adversarial Domain Adaptation","created":"2020-07-07","cited_by_count":99},{"id":"1904.01886","title":"DADA: Depth-aware Domain Adaptation in Semantic Segmentation","created":"2019-04-03","cited_by_count":97},{"id":"1711.11556","title":"ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes","created":"2017-11-30","cited_by_count":95},{"id":"1811.06042","title":"Unsupervised domain adaptation for medical imaging segmentation with self-ensembling","created":"2018-11-14","cited_by_count":94},{"id":"1909.00597","title":"Self-Training and Adversarial Background Regularization for Unsupervised Domain Adaptive One-Stage Object Detection","created":"2019-09-02","cited_by_count":83},{"id":"2105.08128","title":"PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency Training","created":"2021-05-17","cited_by_count":80},{"id":"2004.08514","title":"DMT: Dynamic Mutual Training for Semi-Supervised Learning","created":"2020-04-18","cited_by_count":77},{"id":"1904.00876","title":"Significance-aware Information Bottleneck for Domain Adaptive Semantic Segmentation","created":"2019-04-01","cited_by_count":74},{"id":"2007.02424","title":"Contextual-Relation Consistent Domain Adaptation for Semantic Segmentation","created":"2020-07-05","cited_by_count":70},{"id":"2101.01461","title":"PointCutMix: Regularization Strategy for Point Cloud Classification","created":"2021-01-05","cited_by_count":69},{"id":"2003.04614","title":"Label-Driven Reconstruction for Domain Adaptation in Semantic Segmentation","created":"2020-03-10","cited_by_count":62},{"id":"2004.04923","title":"Phase Consistent Ecological Domain Adaptation","created":"2020-04-10","cited_by_count":62},{"id":"2012.04828","title":"Two-phase Pseudo Label Densification for Self-training based Domain Adaptation","created":"2020-12-09","cited_by_count":60},{"id":"1912.11164","title":"Unsupervised Scene Adaptation with Memory Regularization in vivo","created":"2019-12-24","cited_by_count":57},{"id":"2007.15176","title":"Domain Adaptive Semantic Segmentation Using Weak Labels","created":"2020-07-30","cited_by_count":56},{"id":"1912.08954","title":"An Adversarial Perturbation Oriented Domain Adaptation Approach for Semantic Segmentation","created":"2019-12-18","cited_by_count":48},{"id":"2004.08878","title":"Uncertainty-Aware Consistency Regularization for Cross-Domain Semantic Segmentation","created":"2020-04-19","cited_by_count":45},{"id":"2106.03422","title":"Source-Free Open Compound Domain Adaptation in Semantic Segmentation","created":"2021-06-07","cited_by_count":43},{"id":"1906.03502","title":"Attending to Discriminative Certainty for Domain Adaptation","created":"2019-06-08","cited_by_count":37},{"id":"2003.06883","title":"Night-time Scene Parsing with a Large Real Dataset","created":"2020-03-15","cited_by_count":37},{"id":"2003.04010","title":"Context-Aware Domain Adaptation in Semantic Segmentation","created":"2020-03-09","cited_by_count":36},{"id":"2003.12022","title":"Milking CowMask for Semi-Supervised Image Classification","created":"2020-03-26","cited_by_count":35},{"id":"2009.12559","title":"Affinity Space Adaptation for Semantic Segmentation Across Domains","created":"2020-09-26","cited_by_count":31},{"id":"2108.03267","title":"BiMaL: Bijective Maximum Likelihood Approach to Domain Adaptation in Semantic Scene Segmentation","created":"2021-08-06","cited_by_count":31},{"id":"1809.00903","title":"Penalizing Top Performers: Conservative Loss for Semantic Segmentation Adaptation","created":"2018-09-04","cited_by_count":29},{"id":"2006.08658","title":"ESL: Entropy-guided Self-supervised Learning for Domain Adaptation in Semantic Segmentation","created":"2020-06-15","cited_by_count":27},{"id":"1906.09693","title":"Bayesian Uncertainty Matching for Unsupervised Domain Adaptation","created":"2019-06-24","cited_by_count":22},{"id":"1905.04215","title":"Virtual Mixup Training for Unsupervised Domain Adaptation","created":"2019-05-10","cited_by_count":21},{"id":"1907.11202","title":"Unsupervised Domain Adaptation via Calibrating Uncertainties","created":"2019-07-25","cited_by_count":15}],"next_cursor":null}