{"list":"other","total":112,"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":"2002.05709","title":"A Simple Framework for Contrastive Learning of Visual Representations","created":"2020-02-13","cited_by_count":12049},{"id":"1503.02531","title":"Distilling the Knowledge in a Neural Network","created":"2015-03-09","cited_by_count":10324},{"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":"1602.05629","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","created":"2016-02-17","cited_by_count":7669},{"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":"1604.01685","title":"The Cityscapes Dataset for Semantic Urban Scene Understanding","created":"2016-04-06","cited_by_count":4444},{"id":"1802.03426","title":"UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction","created":"2018-02-09","cited_by_count":2898},{"id":"1611.05431","title":"Aggregated Residual Transformations for Deep Neural Networks","created":"2016-11-16","cited_by_count":2546},{"id":"1909.13719","title":"RandAugment: Practical automated data augmentation with a reduced search space","created":"2019-09-30","cited_by_count":2269},{"id":"2001.07685","title":"FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence","created":"2020-01-21","cited_by_count":2085},{"id":"1708.04552","title":"Improved Regularization of Convolutional Neural Networks with Cutout","created":"2017-08-15","cited_by_count":1628},{"id":"1904.01355","title":"FCOS: Fully Convolutional One-Stage Object Detection","created":"2019-04-02","cited_by_count":1577},{"id":"1803.08494","title":"Group Normalization","created":"2018-03-22","cited_by_count":1524},{"id":"1706.02677","title":"Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour","created":"2017-06-08","cited_by_count":1492},{"id":"1703.06211","title":"Deformable Convolutional Networks","created":"2017-03-17","cited_by_count":1488},{"id":"1905.02249","title":"MixMatch: A Holistic Approach to Semi-Supervised Learning","created":"2019-05-06","cited_by_count":1391},{"id":"1902.09212","title":"Deep High-Resolution Representation Learning for Human Pose Estimation","created":"2019-02-25","cited_by_count":1339},{"id":"1911.02685","title":"A Comprehensive Survey on Transfer Learning","created":"2019-11-07","cited_by_count":1160},{"id":"1710.03740","title":"Mixed Precision Training","created":"2017-10-10","cited_by_count":1156},{"id":"1611.08050","title":"Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields","created":"2016-11-24","cited_by_count":1064},{"id":"1912.01991","title":"Self-Supervised Learning of Pretext-Invariant Representations","created":"2019-12-04","cited_by_count":808},{"id":"1802.03601","title":"Deep Visual Domain Adaptation: A Survey","created":"2018-02-10","cited_by_count":635},{"id":"1802.10349","title":"Learning to Adapt Structured Output Space for Semantic Segmentation","created":"2018-02-28","cited_by_count":584},{"id":"1802.00434","title":"DensePose: Dense Human Pose Estimation In The Wild","created":"2018-02-01","cited_by_count":575},{"id":"2102.07623","title":"FedBN: Federated Learning on Non-IID Features via Local Batch Normalization","created":"2021-02-15","cited_by_count":571},{"id":"1804.06208","title":"Simple Baselines for Human Pose Estimation and Tracking","created":"2018-04-17","cited_by_count":502},{"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":"1803.03243","title":"Domain Adaptive Faster R-CNN for Object Detection in the Wild","created":"2018-03-08","cited_by_count":443},{"id":"1606.04586","title":"Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning","created":"2016-06-14","cited_by_count":441},{"id":"1906.09756","title":"Cascade R-CNN: High Quality Object Detection and Instance Segmentation","created":"2019-06-24","cited_by_count":429},{"id":"1908.09822","title":"Confidence Regularized Self-Training","created":"2019-08-26","cited_by_count":419},{"id":"1911.09785","title":"ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring","created":"2019-11-21","cited_by_count":349},{"id":"1711.07319","title":"Cascaded Pyramid Network for Multi-Person Pose Estimation","created":"2017-11-20","cited_by_count":341},{"id":"1612.00137","title":"RMPE: Regional Multi-person Pose Estimation","created":"2016-12-01","cited_by_count":340},{"id":"1808.07371","title":"Everybody Dance Now","created":"2018-08-22","cited_by_count":315},{"id":"1904.10620","title":"Bidirectional Learning for Domain Adaptation of Semantic Segmentation","created":"2019-04-24","cited_by_count":308},{"id":"2102.09480","title":"Unbiased Teacher for Semi-Supervised Object Detection","created":"2021-02-18","cited_by_count":306},{"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":"1711.08447","title":"VITON: An Image-based Virtual Try-on Network","created":"2017-11-22","cited_by_count":286},{"id":"1911.09665","title":"Adversarial Examples Improve Image Recognition","created":"2019-11-21","cited_by_count":286},{"id":"2005.04757","title":"A Simple Semi-Supervised Learning Framework for Object Detection","created":"2020-05-10","cited_by_count":282},{"id":"1812.04798","title":"Strong-Weak Distribution Alignment for Adaptive Object Detection","created":"2018-12-12","cited_by_count":270},{"id":"1908.10357","title":"HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation","created":"2019-08-27","cited_by_count":247},{"id":"1612.02649","title":"FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation","created":"2016-12-08","cited_by_count":243},{"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":"1803.11365","title":"Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation","created":"2018-03-30","cited_by_count":218},{"id":"1611.05424","title":"Associative Embedding: End-to-End Learning for Joint Detection and Grouping","created":"2016-11-16","cited_by_count":212},{"id":"1911.06667","title":"CenterMask : Real-Time Anchor-Free Instance Segmentation","created":"2019-11-15","cited_by_count":201},{"id":"1806.00451","title":"Do CIFAR-10 Classifiers Generalize to CIFAR-10?","created":"2018-06-01","cited_by_count":200},{"id":"1906.03950","title":"Domain-Specific Batch Normalization for Unsupervised Domain Adaptation","created":"2019-05-27","cited_by_count":197},{"id":"2003.00707","title":"Unbiased Mean Teacher for Cross-domain Object Detection","created":"2020-03-02","cited_by_count":159},{"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":"1810.04304","title":"Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation","created":"2018-10-10","cited_by_count":149},{"id":"1903.00348","title":"Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation","created":"2019-02-28","cited_by_count":140},{"id":"1904.11245","title":"Exploring Object Relation in Mean Teacher for Cross-Domain Detection","created":"2019-04-25","cited_by_count":137},{"id":"1803.08225","title":"PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model","created":"2018-03-22","cited_by_count":133},{"id":"1712.04440","title":"Data Distillation: Towards Omni-Supervised Learning","created":"2017-12-12","cited_by_count":126},{"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":"1901.08211","title":"Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation","created":"2019-01-24","cited_by_count":121},{"id":"1903.06593","title":"PifPaf: Composite Fields for Human Pose Estimation","created":"2019-03-15","cited_by_count":120},{"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":"1904.02361","title":"A Robust Learning Approach to Domain Adaptive Object Detection","created":"2019-04-04","cited_by_count":109},{"id":"1803.10683","title":"Pose2Seg: Detection Free Human Instance Segmentation","created":"2018-03-28","cited_by_count":107},{"id":"2001.03182","title":"CrDoCo: Pixel-level Domain Transfer with Cross-Domain Consistency","created":"2020-01-09","cited_by_count":107},{"id":"2008.08574","title":"Every Pixel Matters: Center-aware Feature Alignment for Domain Adaptive Object Detector","created":"2020-08-19","cited_by_count":107},{"id":"2103.04224","title":"MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection","created":"2021-03-07","cited_by_count":106},{"id":"1903.12174","title":"TensorMask: A Foundation for Dense Object Segmentation","created":"2019-03-28","cited_by_count":101},{"id":"2009.00155","title":"A Review of Single-Source Deep Unsupervised Visual Domain Adaptation","created":"2020-09-01","cited_by_count":96},{"id":"2105.13502","title":"Unsupervised Domain Adaptation of Object Detectors: A Survey","created":"2021-05-27","cited_by_count":93},{"id":"1912.02801","title":"PolyTransform: Deep Polygon Transformer for Instance Segmentation","created":"2019-12-05","cited_by_count":92},{"id":"2005.10876","title":"Unsupervised Domain Adaptation in Semantic Segmentation: a Review","created":"2020-05-21","cited_by_count":88},{"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":"1611.08303","title":"Deep Watershed Transform for Instance Segmentation","created":"2016-11-24","cited_by_count":78},{"id":"1706.05274","title":"Perceptual Generative Adversarial Networks for Small Object Detection","created":"2017-06-16","cited_by_count":76},{"id":"1811.05419","title":"Fast Human Pose Estimation","created":"2018-11-13","cited_by_count":68},{"id":"1711.07240","title":"MegDet: A Large Mini-Batch Object Detector","created":"2017-11-20","cited_by_count":66},{"id":"1904.07305","title":"Automatic adaptation of object detectors to new domains using self-training","created":"2019-04-15","cited_by_count":66},{"id":"2007.07222","title":"Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis","created":"2020-07-05","cited_by_count":60},{"id":"1811.09998","title":"Low-resolution Face Recognition in the Wild via Selective Knowledge Distillation","created":"2018-11-25","cited_by_count":55},{"id":"1803.11316","title":"Task-Driven Super Resolution: Object Detection in Low-resolution Images","created":"2018-03-30","cited_by_count":53},{"id":"1911.07982","title":"Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling","created":"2019-11-18","cited_by_count":53},{"id":"2004.11500","title":"What Can Be Transferred: Unsupervised Domain Adaptation for Endoscopic Lesions Segmentation","created":"2020-04-24","cited_by_count":50},{"id":"1611.08272","title":"InstanceCut: from Edges to Instances with MultiCut","created":"2016-11-24","cited_by_count":49},{"id":"1808.08180","title":"MVOR: A Multi-view RGB-D Operating Room Dataset for 2D and 3D Human Pose Estimation","created":"2018-08-24","cited_by_count":49},{"id":"1911.07451","title":"DirectPose: Direct End-to-End Multi-Person Pose Estimation","created":"2019-11-18","cited_by_count":49},{"id":"1707.05388","title":"Benchmarking and Error Diagnosis in Multi-Instance Pose Estimation","created":"2017-07-17","cited_by_count":48},{"id":"2105.07576","title":"Rethinking \"Batch\" in BatchNorm","created":"2021-05-17","cited_by_count":45},{"id":"2009.08119","title":"Collaborative Training between Region Proposal Localization and Classification for Domain Adaptive Object Detection","created":"2020-09-17","cited_by_count":44},{"id":"1604.03196","title":"Privacy-Preserving Human Activity Recognition from Extreme Low Resolution","created":"2016-04-12","cited_by_count":43},{"id":"1910.09031","title":"CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer Assisted Interventions","created":"2019-10-20","cited_by_count":40},{"id":"1601.04153","title":"Studying Very Low Resolution Recognition Using Deep Networks","created":"2016-01-16","cited_by_count":36},{"id":"2006.16166","title":"Automatic Operating Room Surgical Activity Recognition for Robot-Assisted Surgery","created":"2020-06-29","cited_by_count":34},{"id":"2105.14185","title":"FCPose: Fully Convolutional Multi-Person Pose Estimation with Dynamic Instance-Aware Convolutions","created":"2021-05-29","cited_by_count":34},{"id":"1906.03787","title":"Intriguing properties of adversarial training at scale","created":"2019-06-10","cited_by_count":33},{"id":"1907.02766","title":"Data Efficient Unsupervised Domain Adaptation for Cross-Modality Image Segmentation","created":"2019-07-05","cited_by_count":33}],"next_cursor":"100"}