{"list":"other","total":54,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"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":"1506.02640","title":"You Only Look Once: Unified, Real-Time Object Detection","created":"2015-06-08","cited_by_count":4852},{"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":"1512.02325","title":"SSD: Single Shot MultiBox Detector","created":"2015-12-08","cited_by_count":3130},{"id":"1504.08083","title":"Fast R-CNN","created":"2015-04-30","cited_by_count":2933},{"id":"1804.02767","title":"YOLOv3: An Incremental Improvement","created":"2018-04-08","cited_by_count":2659},{"id":"1512.04150","title":"Learning Deep Features for Discriminative Localization","created":"2015-12-14","cited_by_count":2436},{"id":"1311.2524","title":"Rich feature hierarchies for accurate object detection and semantic segmentation","created":"2013-11-11","cited_by_count":2162},{"id":"2004.10934","title":"YOLOv4: Optimal Speed and Accuracy of Object Detection","created":"2020-04-23","cited_by_count":1630},{"id":"1811.12231","title":"ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness","created":"2018-11-29","cited_by_count":1432},{"id":"1612.08242","title":"YOLO9000: Better, Faster, Stronger","created":"2016-12-25","cited_by_count":1431},{"id":"1805.04687","title":"BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning","created":"2018-05-12","cited_by_count":1288},{"id":"1608.02192","title":"Playing for Data: Ground Truth from Computer Games","created":"2016-08-07","cited_by_count":818},{"id":"1703.10593","title":"Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks","created":"2017-03-30","cited_by_count":707},{"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":"1908.09822","title":"Confidence Regularized Self-Training","created":"2019-08-26","cited_by_count":419},{"id":"1604.05144","title":"ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation","created":"2016-04-18","cited_by_count":331},{"id":"2004.04581","title":"Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation","created":"2020-04-09","cited_by_count":300},{"id":"1506.02106","title":"What's the Point: Semantic Segmentation with Point Supervision","created":"2015-06-06","cited_by_count":298},{"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":"1503.01640","title":"BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation","created":"2015-03-05","cited_by_count":238},{"id":"1603.07485","title":"Simple Does It: Weakly Supervised Instance and Semantic Segmentation","created":"2016-03-24","cited_by_count":227},{"id":"2007.08702","title":"DACS: Domain Adaptation via Cross-domain Mixed Sampling","created":"2020-07-17","cited_by_count":226},{"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":"1910.13049","title":"Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation","created":"2019-10-29","cited_by_count":151},{"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":"2105.08965","title":"Railroad is not a Train: Saliency as Pseudo-pixel Supervision for Weakly Supervised Semantic Segmentation","created":"2021-05-19","cited_by_count":127},{"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":"1803.09569","title":"On Regularized Losses for Weakly-supervised CNN Segmentation","created":"2018-03-26","cited_by_count":121},{"id":"1804.01346","title":"Normalized Cut Loss for Weakly-supervised CNN Segmentation","created":"2018-04-04","cited_by_count":112},{"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":"1908.09547","title":"Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach","created":"2019-08-26","cited_by_count":103},{"id":"1802.00470","title":"Learning random-walk label propagation for weakly-supervised semantic segmentation","created":"2018-02-01","cited_by_count":101},{"id":"1904.11693","title":"Box-driven Class-wise Region Masking and Filling Rate Guided Loss for Weakly Supervised Semantic Segmentation","created":"2019-04-26","cited_by_count":98},{"id":"2105.08128","title":"PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency Training","created":"2021-05-17","cited_by_count":80},{"id":"1904.00876","title":"Significance-aware Information Bottleneck for Domain Adaptive Semantic Segmentation","created":"2019-04-01","cited_by_count":74},{"id":"2003.04614","title":"Label-Driven Reconstruction for Domain Adaptation in Semantic Segmentation","created":"2020-03-10","cited_by_count":62},{"id":"2105.06464","title":"DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision","created":"2021-05-13","cited_by_count":59},{"id":"1912.08954","title":"An Adversarial Perturbation Oriented Domain Adaptation Approach for Semantic Segmentation","created":"2019-12-18","cited_by_count":48},{"id":"2108.12995","title":"Pseudo-mask Matters in Weakly-supervised Semantic Segmentation","created":"2021-08-30","cited_by_count":48},{"id":"2203.09744","title":"Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic Segmentation","created":"2022-03-18","cited_by_count":47},{"id":"2103.05254","title":"MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentation","created":"2021-03-09","cited_by_count":43},{"id":"1808.03575","title":"Weakly- and Semi-Supervised Panoptic Segmentation","created":"2018-08-10","cited_by_count":42},{"id":"1910.11645","title":"Reducing Domain Gap by Reducing Style Bias","created":"2019-10-25","cited_by_count":35},{"id":"2012.06995","title":"Bi-Classifier Determinacy Maximization for Unsupervised Domain Adaptation","created":"2020-12-13","cited_by_count":35},{"id":"1811.02233","title":"Weakly Supervised Scene Parsing with Point-based Distance Metric Learning","created":"2018-11-06","cited_by_count":33}],"next_cursor":null}