{"list":"other","total":53,"rows":[{"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":"1405.0312","title":"Microsoft COCO: Common Objects in Context","created":"2014-05-01","cited_by_count":17608},{"id":"1506.01497","title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","created":"2015-06-04","cited_by_count":9639},{"id":"1911.05722","title":"Momentum Contrast for Unsupervised Visual Representation Learning","created":"2019-11-13","cited_by_count":7513},{"id":"1703.06870","title":"Mask R-CNN","created":"2017-03-20","cited_by_count":7277},{"id":"1612.03144","title":"Feature Pyramid Networks for Object Detection","created":"2016-12-09","cited_by_count":5036},{"id":"2012.12877","title":"Training data-efficient image transformers & distillation through attention","created":"2020-12-23","cited_by_count":4586},{"id":"2003.04297","title":"Improved Baselines with Momentum Contrastive Learning","created":"2020-03-09","cited_by_count":2287},{"id":"2004.10934","title":"YOLOv4: Optimal Speed and Accuracy of Object Detection","created":"2020-04-23","cited_by_count":1630},{"id":"1708.04552","title":"Improved Regularization of Convolutional Neural Networks with Cutout","created":"2017-08-15","cited_by_count":1628},{"id":"1612.08242","title":"YOLO9000: Better, Faster, Stronger","created":"2016-12-25","cited_by_count":1431},{"id":"1906.07155","title":"MMDetection: Open MMLab Detection Toolbox and Benchmark","created":"2019-06-17","cited_by_count":1385},{"id":"1908.03195","title":"LVIS: A Dataset for Large Vocabulary Instance Segmentation","created":"2019-08-08","cited_by_count":1217},{"id":"1707.08114","title":"A Survey on Multi-Task Learning","created":"2017-07-25","cited_by_count":840},{"id":"1910.09217","title":"Decoupling Representation and Classifier for Long-Tailed Recognition","created":"2019-10-21","cited_by_count":809},{"id":"1804.08328","title":"Taskonomy: Disentangling Task Transfer Learning","created":"2018-04-23","cited_by_count":634},{"id":"1706.00384","title":"Deep Mutual Learning","created":"2017-06-01","cited_by_count":628},{"id":"2201.02605","title":"Detecting Twenty-thousand Classes using Image-level Supervision","created":"2022-01-07","cited_by_count":614},{"id":"2006.06882","title":"Rethinking Pre-training and Self-training","created":"2020-06-11","cited_by_count":352},{"id":"2102.09480","title":"Unbiased Teacher for Semi-Supervised Object Detection","created":"2021-02-18","cited_by_count":306},{"id":"2003.05176","title":"Equalization Loss for Long-Tailed Object Recognition","created":"2020-03-11","cited_by_count":285},{"id":"2005.04757","title":"A Simple Semi-Supervised Learning Framework for Object Detection","created":"2020-05-10","cited_by_count":282},{"id":"2009.12991","title":"Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect","created":"2020-09-28","cited_by_count":256},{"id":"2103.16370","title":"Distribution Alignment: A Unified Framework for Long-tail Visual Recognition","created":"2021-03-30","cited_by_count":198},{"id":"1511.02853","title":"Weakly Supervised Deep Detection Networks","created":"2015-11-09","cited_by_count":183},{"id":"2006.10408","title":"Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax","created":"2020-06-18","cited_by_count":152},{"id":"1803.05984","title":"Deep Co-Training for Semi-Supervised Image Recognition","created":"2018-03-15","cited_by_count":147},{"id":"2008.10032","title":"Seesaw Loss for Long-Tailed Instance Segmentation","created":"2020-08-23","cited_by_count":146},{"id":"1704.00138","title":"Multiple Instance Detection Network with Online Instance Classifier Refinement","created":"2017-04-01","cited_by_count":117},{"id":"2103.11402","title":"Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework","created":"2021-03-21","cited_by_count":113},{"id":"2007.11978","title":"The Devil is in Classification: A Simple Framework for Long-tail Object Detection and Instance Segmentation","created":"2020-07-23","cited_by_count":108},{"id":"2006.04096","title":"Robust Learning Through Cross-Task Consistency","created":"2020-06-07","cited_by_count":105},{"id":"2012.08548","title":"Equalization Loss v2: A New Gradient Balance Approach for Long-tailed Object Detection","created":"2020-12-15","cited_by_count":99},{"id":"1807.03342","title":"PCL: Proposal Cluster Learning for Weakly Supervised Object Detection","created":"2018-07-09","cited_by_count":97},{"id":"2004.04725","title":"Instance-aware, Context-focused, and Memory-efficient Weakly Supervised Object Detection","created":"2020-04-09","cited_by_count":80},{"id":"2109.04075","title":"Self Supervision to Distillation for Long-Tailed Visual Recognition","created":"2021-09-09","cited_by_count":73},{"id":"2102.01066","title":"Evaluating Large-Vocabulary Object Detectors: The Devil is in the Details","created":"2021-02-01","cited_by_count":57},{"id":"2104.00885","title":"Adaptive Class Suppression Loss for Long-Tail Object Detection","created":"2021-04-02","cited_by_count":55},{"id":"2004.00900","title":"Learning to Segment the Tail","created":"2020-04-02","cited_by_count":54},{"id":"1909.04972","title":"WSOD^2: Learning Bottom-up and Top-down Objectness Distillation for Weakly-supervised Object Detection","created":"2019-09-11","cited_by_count":51},{"id":"2205.08303","title":"MulT: An End-to-End Multitask Learning Transformer","created":"2022-05-17","cited_by_count":48},{"id":"1611.08258","title":"Weakly Supervised Cascaded Convolutional Networks","created":"2016-11-24","cited_by_count":47},{"id":"2201.02593","title":"Equalized Focal Loss for Dense Long-Tailed Object Detection","created":"2022-01-07","cited_by_count":46},{"id":"1503.00949","title":"Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning","created":"2015-03-03","cited_by_count":39},{"id":"2107.02170","title":"On Model Calibration for Long-Tailed Object Detection and Instance Segmentation","created":"2021-07-05","cited_by_count":39},{"id":"1812.00124","title":"NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection","created":"2018-12-01","cited_by_count":37},{"id":"2102.08884","title":"MosaicOS: A Simple and Effective Use of Object-Centric Images for Long-Tailed Object Detection","created":"2021-02-17","cited_by_count":33},{"id":"2007.07986","title":"Boosting Weakly Supervised Object Detection with Progressive Knowledge Transfer","created":"2020-07-15","cited_by_count":28},{"id":"2104.05702","title":"Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection","created":"2021-04-12","cited_by_count":27},{"id":"1708.06128","title":"Revisiting knowledge transfer for training object class detectors","created":"2017-08-21","cited_by_count":26},{"id":"1910.11012","title":"Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition","created":"2019-10-24","cited_by_count":26},{"id":"2201.02784","title":"Relieving Long-tailed Instance Segmentation via Pairwise Class Balance","created":"2022-01-08","cited_by_count":18}],"next_cursor":null}