{"list":"other","total":52,"rows":[{"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":"2005.12872","title":"End-to-End Object Detection with Transformers","created":"2020-05-26","cited_by_count":7414},{"id":"1411.4038","title":"Fully Convolutional Networks for Semantic Segmentation","created":"2014-11-14","cited_by_count":4859},{"id":"1612.00796","title":"Overcoming catastrophic forgetting in neural networks","created":"2016-12-02","cited_by_count":4787},{"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":"1606.09282","title":"Learning without Forgetting","created":"2016-06-29","cited_by_count":2496},{"id":"1611.07725","title":"iCaRL: Incremental Classifier and Representation Learning","created":"2016-11-23","cited_by_count":2333},{"id":"1706.05587","title":"Rethinking Atrous Convolution for Semantic Image Segmentation","created":"2017-06-17","cited_by_count":2161},{"id":"2102.12122","title":"Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions","created":"2021-02-24","cited_by_count":1962},{"id":"2012.15840","title":"Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers","created":"2020-12-31","cited_by_count":1369},{"id":"2107.06278","title":"Per-Pixel Classification is Not All You Need for Semantic Segmentation","created":"2021-07-13","cited_by_count":1152},{"id":"2101.11986","title":"Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet","created":"2021-01-28","cited_by_count":1101},{"id":"2103.14899","title":"CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification","created":"2021-03-27","cited_by_count":806},{"id":"1807.09536","title":"End-to-End Incremental Learning","created":"2018-07-25","cited_by_count":597},{"id":"1412.7062","title":"Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs","created":"2014-12-22","cited_by_count":554},{"id":"2104.01136","title":"LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference","created":"2021-04-02","cited_by_count":475},{"id":"2103.16788","title":"DER: Dynamically Expandable Representation for Class Incremental Learning","created":"2021-03-31","cited_by_count":355},{"id":"1604.05144","title":"ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation","created":"2016-04-18","cited_by_count":331},{"id":"1506.02106","title":"What's the Point: Semantic Segmentation with Point Supervision","created":"2015-06-06","cited_by_count":298},{"id":"1811.08051","title":"Learning without Memorizing","created":"2018-11-20","cited_by_count":250},{"id":"1503.01640","title":"BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation","created":"2015-03-05","cited_by_count":238},{"id":"2108.05895","title":"Mobile-Former: Bridging MobileNet and Transformer","created":"2021-08-12","cited_by_count":235},{"id":"2003.03773","title":"Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation","created":"2020-03-08","cited_by_count":222},{"id":"2203.03884","title":"Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels","created":"2022-03-08","cited_by_count":218},{"id":"2002.00718","title":"Modeling the Background for Incremental Learning in Semantic Segmentation","created":"2020-02-03","cited_by_count":190},{"id":"2106.05095","title":"ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation","created":"2021-06-09","cited_by_count":184},{"id":"2011.11390","title":"PLOP: Learning without Forgetting for Continual Semantic Segmentation","created":"2020-11-23","cited_by_count":177},{"id":"1903.07864","title":"Class-incremental Learning via Deep Model Consolidation","created":"2019-03-19","cited_by_count":173},{"id":"2010.05063","title":"Adaptive Aggregation Networks for Class-Incremental Learning","created":"2020-10-10","cited_by_count":149},{"id":"1907.13372","title":"Incremental Learning Techniques for Semantic Segmentation","created":"2019-07-31","cited_by_count":141},{"id":"1909.13589","title":"Domain Adaptation for Semantic Segmentation with Maximum Squares Loss","created":"2019-09-30","cited_by_count":138},{"id":"2106.09701","title":"Always Be Dreaming: A New Approach for Data-Free Class-Incremental Learning","created":"2021-06-17","cited_by_count":131},{"id":"2103.08907","title":"BBAM: Bounding Box Attribution Map for Weakly Supervised Semantic and Instance Segmentation","created":"2021-03-16","cited_by_count":105},{"id":"2103.06342","title":"Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations","created":"2021-03-10","cited_by_count":96},{"id":"2108.03673","title":"RECALL: Replay-based Continual Learning in Semantic Segmentation","created":"2021-08-08","cited_by_count":83},{"id":"1909.09408","title":"ACFNet: Attentional Class Feature Network for Semantic Segmentation","created":"2019-09-20","cited_by_count":75},{"id":"2106.11562","title":"SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning","created":"2021-06-22","cited_by_count":71},{"id":"1903.11233","title":"Deep Co-Training for Semi-Supervised Image Segmentation","created":"2019-03-27","cited_by_count":58},{"id":"2102.09517","title":"Essentials for Class Incremental Learning","created":"2021-02-18","cited_by_count":57},{"id":"2110.04065","title":"Test-time Batch Statistics Calibration for Covariate Shift","created":"2021-10-06","cited_by_count":50},{"id":"2104.12166","title":"MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learning","created":"2021-04-25","cited_by_count":45},{"id":"2103.13041","title":"Coarse-to-Fine Domain Adaptive Semantic Segmentation with Photometric Alignment and Category-Center Regularization","created":"2021-03-24","cited_by_count":36},{"id":"2012.03362","title":"Self-Training for Class-Incremental Semantic Segmentation","created":"2020-12-06","cited_by_count":32},{"id":"2110.06484","title":"Domain Adaptive Semantic Segmentation without Source Data","created":"2021-10-13","cited_by_count":30},{"id":"2102.09896","title":"Scribble-Supervised Semantic Segmentation by Uncertainty Reduction on Neural Representation and Self-Supervision on Neural Eigenspace","created":"2021-02-19","cited_by_count":19},{"id":"2104.13614","title":"Preserving Earlier Knowledge in Continual Learning with the Help of All Previous Feature Extractors","created":"2021-04-28","cited_by_count":19},{"id":"2007.05615","title":"PCAMs: Weakly Supervised Semantic Segmentation Using Point Supervision","created":"2020-07-10","cited_by_count":15},{"id":"2106.15287","title":"Tackling Catastrophic Forgetting and Background Shift in Continual Semantic Segmentation","created":"2021-06-29","cited_by_count":15}],"next_cursor":null}