{"list":"other","total":53,"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":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"id":"2002.05709","title":"A Simple Framework for Contrastive Learning of Visual Representations","created":"2020-02-13","cited_by_count":12049},{"id":"1807.03748","title":"Representation Learning with Contrastive Predictive Coding","created":"2018-07-10","cited_by_count":7777},{"id":"1911.05722","title":"Momentum Contrast for Unsupervised Visual Representation Learning","created":"2019-11-13","cited_by_count":7513},{"id":"1612.00796","title":"Overcoming catastrophic forgetting in neural networks","created":"2016-12-02","cited_by_count":4787},{"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":"2105.15203","title":"SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers","created":"2021-05-31","cited_by_count":2940},{"id":"2112.01527","title":"Masked-attention Mask Transformer for Universal Image Segmentation","created":"2021-12-02","cited_by_count":2351},{"id":"1611.07725","title":"iCaRL: Incremental Classifier and Representation Learning","created":"2016-11-23","cited_by_count":2333},{"id":"2003.04297","title":"Improved Baselines with Momentum Contrastive Learning","created":"2020-03-09","cited_by_count":2287},{"id":"1706.05587","title":"Rethinking Atrous Convolution for Semantic Image Segmentation","created":"2017-06-17","cited_by_count":2161},{"id":"1706.08840","title":"Gradient Episodic Memory for Continual Learning","created":"2017-06-26","cited_by_count":1898},{"id":"2104.02057","title":"An Empirical Study of Training Self-Supervised Vision Transformers","created":"2021-04-05","cited_by_count":1616},{"id":"2107.06278","title":"Per-Pixel Classification is Not All You Need for Semantic Segmentation","created":"2021-07-13","cited_by_count":1152},{"id":"1904.05160","title":"Large-Scale Long-Tailed Recognition in an Open World","created":"2019-04-10","cited_by_count":705},{"id":"2102.06183","title":"Less is More: ClipBERT for Video-and-Language Learning via Sparse Sampling","created":"2021-02-11","cited_by_count":484},{"id":"2007.10740","title":"Balanced Meta-Softmax for Long-Tailed Visual Recognition","created":"2020-07-21","cited_by_count":400},{"id":"2111.14887","title":"DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation","created":"2021-11-29","cited_by_count":300},{"id":"2103.02603","title":"Towards Open World Object Detection","created":"2021-03-03","cited_by_count":297},{"id":"2009.09687","title":"Contrastive Clustering","created":"2020-09-21","cited_by_count":265},{"id":"2107.12028","title":"Parametric Contrastive Learning","created":"2021-07-26","cited_by_count":217},{"id":"1708.06977","title":"Incremental Learning of Object Detectors without Catastrophic Forgetting","created":"2017-08-23","cited_by_count":208},{"id":"2002.00718","title":"Modeling the Background for Incremental Learning in Semantic Segmentation","created":"2020-02-03","cited_by_count":190},{"id":"2011.11390","title":"PLOP: Learning without Forgetting for Continual Semantic Segmentation","created":"2020-11-23","cited_by_count":177},{"id":"2105.00097","title":"Self-supervised Augmentation Consistency for Adapting Semantic Segmentation","created":"2021-04-30","cited_by_count":149},{"id":"2008.10032","title":"Seesaw Loss for Long-Tailed Instance Segmentation","created":"2020-08-23","cited_by_count":146},{"id":"2207.09052","title":"Balanced Contrastive Learning for Long-Tailed Visual Recognition","created":"2022-07-19","cited_by_count":144},{"id":"1907.13372","title":"Incremental Learning Techniques for Semantic Segmentation","created":"2019-07-31","cited_by_count":141},{"id":"2104.08572","title":"On Learning the Geodesic Path for Incremental Learning","created":"2021-04-17","cited_by_count":85},{"id":"2203.05402","title":"Representation Compensation Networks for Continual Semantic Segmentation","created":"2022-03-10","cited_by_count":72},{"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":"2012.04324","title":"Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning","created":"2020-12-08","cited_by_count":55},{"id":"1904.02749","title":"Learning to Cluster Faces on an Affinity Graph","created":"2019-04-04","cited_by_count":50},{"id":"2004.00445","title":"Learning to Cluster Faces via Confidence and Connectivity Estimation","created":"2020-04-01","cited_by_count":42},{"id":"2204.07596","title":"Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning","created":"2022-04-15","cited_by_count":40},{"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":"2304.02135","title":"FREDOM: Fairness Domain Adaptation Approach to Semantic Scene Understanding","created":"2023-04-04","cited_by_count":27},{"id":"2203.07667","title":"SATS: Self-Attention Transfer for Continual Semantic Segmentation","created":"2022-03-15","cited_by_count":25},{"id":"2211.13999","title":"CoMFormer: Continual Learning in Semantic and Panoptic Segmentation","created":"2022-11-25","cited_by_count":25},{"id":"1806.00265","title":"Learn the new, keep the old: Extending pretrained models with new anatomy and images","created":"2018-06-01","cited_by_count":24},{"id":"1811.04634","title":"Extending Pretrained Segmentation Networks with Additional Anatomical Structures","created":"2018-11-12","cited_by_count":22},{"id":"2206.14085","title":"Continual Learning with Transformers for Image Classification","created":"2022-06-28","cited_by_count":17},{"id":"2111.00531","title":"Learning Debiased and Disentangled Representations for Semantic Segmentation","created":"2021-10-31","cited_by_count":16},{"id":"2305.15700","title":"Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments","created":"2023-05-25","cited_by_count":16},{"id":"2305.15699","title":"Cross-view Action Recognition Understanding From Exocentric to Egocentric Perspective","created":"2023-05-25","cited_by_count":14},{"id":"2103.14051","title":"Tilted Cross Entropy (TCE): Promoting Fairness in Semantic Segmentation","created":"2021-03-25","cited_by_count":12},{"id":"2204.11667","title":"Multi-Head Distillation for Continual Unsupervised Domain Adaptation in Semantic Segmentation","created":"2022-04-25","cited_by_count":12},{"id":"2205.10738","title":"OTAdapt: Optimal Transport-based Approach For Unsupervised Domain Adaptation","created":"2022-05-22","cited_by_count":12},{"id":"2206.03431","title":"Self-supervised Domain Adaptation in Crowd Counting","created":"2022-06-07","cited_by_count":11},{"id":"2212.00621","title":"CONDA: Continual Unsupervised Domain Adaptation Learning in Visual Perception for Self-Driving Cars","created":"2022-12-01","cited_by_count":11}],"next_cursor":null}