{"list":"other","total":72,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1505.04597","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","created":"2015-05-18","cited_by_count":19713},{"id":"1405.0312","title":"Microsoft COCO: Common Objects in Context","created":"2014-05-01","cited_by_count":17608},{"id":"1502.03167","title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","created":"2015-02-11","cited_by_count":7005},{"id":"1612.03144","title":"Feature Pyramid Networks for Object Detection","created":"2016-12-09","cited_by_count":5036},{"id":"1411.4038","title":"Fully Convolutional Networks for Semantic Segmentation","created":"2014-11-14","cited_by_count":4859},{"id":"1801.04381","title":"MobileNetV2: Inverted Residuals and Linear Bottlenecks","created":"2018-01-13","cited_by_count":4795},{"id":"1506.02142","title":"Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning","created":"2015-06-06","cited_by_count":4448},{"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":"1802.02611","title":"Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation","created":"2018-02-07","cited_by_count":3223},{"id":"1905.04899","title":"CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features","created":"2019-05-13","cited_by_count":2884},{"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":"2001.07685","title":"FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence","created":"2020-01-21","cited_by_count":2085},{"id":"1905.02244","title":"Searching for MobileNetV3","created":"2019-05-06","cited_by_count":1994},{"id":"1511.00561","title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","created":"2015-11-02","cited_by_count":1487},{"id":"1905.02249","title":"MixMatch: A Holistic Approach to Semi-Supervised Learning","created":"2019-05-06","cited_by_count":1391},{"id":"1703.02910","title":"Deep Bayesian Active Learning with Image Data","created":"2017-03-08","cited_by_count":819},{"id":"1608.02192","title":"Playing for Data: Ground Truth from Computer Games","created":"2016-08-07","cited_by_count":818},{"id":"1904.07734","title":"Three scenarios for continual learning","created":"2019-04-15","cited_by_count":614},{"id":"1906.03671","title":"Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds","created":"2019-06-09","cited_by_count":610},{"id":"2106.01226","title":"Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision","created":"2021-06-02","cited_by_count":463},{"id":"1906.08158","title":"BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning","created":"2019-06-19","cited_by_count":432},{"id":"2003.09005","title":"Semi-Supervised Semantic Segmentation with Cross-Consistency Training","created":"2020-03-19","cited_by_count":376},{"id":"1911.09785","title":"ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring","created":"2019-11-21","cited_by_count":349},{"id":"1710.10628","title":"Variational Continual Learning","created":"2017-10-29","cited_by_count":339},{"id":"1905.03677","title":"Learning Loss for Active Learning","created":"2019-05-09","cited_by_count":336},{"id":"1705.09914","title":"Dilated Residual Networks","created":"2017-05-28","cited_by_count":294},{"id":"1904.00370","title":"Variational Adversarial Active Learning","created":"2019-03-31","cited_by_count":284},{"id":"1806.05034","title":"A Probabilistic U-Net for Segmentation of Ambiguous Images","created":"2018-06-13","cited_by_count":283},{"id":"2007.07936","title":"ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning","created":"2020-07-15","cited_by_count":209},{"id":"1910.02509","title":"REMIND Your Neural Network to Prevent Catastrophic Forgetting","created":"2019-10-06","cited_by_count":197},{"id":"2005.10821","title":"Hierarchical Multi-Scale Attention for Semantic Segmentation","created":"2020-05-21","cited_by_count":183},{"id":"2010.09713","title":"PseudoSeg: Designing Pseudo Labels for Semantic Segmentation","created":"2020-10-19","cited_by_count":180},{"id":"2102.09559","title":"CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning","created":"2021-02-18","cited_by_count":175},{"id":"1908.05724","title":"Semi-Supervised Semantic Segmentation with High- and Low-level Consistency","created":"2019-08-15","cited_by_count":147},{"id":"2003.10780","title":"Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective","created":"2020-03-24","cited_by_count":143},{"id":"1812.01593","title":"Improving Semantic Segmentation via Video Propagation and Label Relaxation","created":"2018-12-04","cited_by_count":134},{"id":"2104.13415","title":"Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory Bank","created":"2021-04-27","cited_by_count":125},{"id":"1910.07153","title":"Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost","created":"2019-10-16","cited_by_count":114},{"id":"2106.14133","title":"Semi-supervised Semantic Segmentation with Directional Context-aware Consistency","created":"2021-06-27","cited_by_count":112},{"id":"1911.11789","title":"ViewAL: Active Learning with Viewpoint Entropy for Semantic Segmentation","created":"2019-11-26","cited_by_count":96},{"id":"2108.09331","title":"Influence Selection for Active Learning","created":"2021-08-20","cited_by_count":84},{"id":"2104.04465","title":"Bootstrapping Semantic Segmentation with Regional Contrast","created":"2021-04-09","cited_by_count":80},{"id":"2103.16130","title":"Active Learning for Deep Object Detection via Probabilistic Modeling","created":"2021-03-30","cited_by_count":79},{"id":"2004.08514","title":"DMT: Dynamic Mutual Training for Semi-Supervised Learning","created":"2020-04-18","cited_by_count":77},{"id":"2104.02324","title":"Multiple instance active learning for object detection","created":"2021-04-06","cited_by_count":77},{"id":"1309.3848","title":"SEEDS: Superpixels Extracted via Energy-Driven Sampling","created":"2013-09-16","cited_by_count":75},{"id":"1806.05473","title":"Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network","created":"2018-06-14","cited_by_count":74},{"id":"2104.04132","title":"Replay in Deep Learning: Current Approaches and Missing Biological Elements","created":"2021-04-01","cited_by_count":72},{"id":"1702.07956","title":"Generative Adversarial Active Learning","created":"2017-02-25","cited_by_count":70},{"id":"1903.03838","title":"BayesOD: A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors","created":"2019-03-09","cited_by_count":68},{"id":"1809.10816","title":"Generative Adversarial Active Learning for Unsupervised Outlier Detection","created":"2018-09-28","cited_by_count":65},{"id":"1911.09168","title":"Active Learning for Deep Detection Neural Networks","created":"2019-11-20","cited_by_count":62},{"id":"2004.04699","title":"Scalable Active Learning for Object Detection","created":"2020-04-09","cited_by_count":58},{"id":"2002.06583","title":"Reinforced active learning for image segmentation","created":"2020-02-16","cited_by_count":56},{"id":"1808.06671","title":"Adversarial Sampling for Active Learning","created":"2018-08-20","cited_by_count":51},{"id":"1810.09726","title":"CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation","created":"2018-10-23","cited_by_count":49},{"id":"1801.05124","title":"Localization-Aware Active Learning for Object Detection","created":"2018-01-16","cited_by_count":48},{"id":"1901.10609","title":"Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector","created":"2019-01-29","cited_by_count":45},{"id":"1703.06452","title":"Algorithms for Semantic Segmentation of Multispectral Remote Sensing Imagery using Deep Learning","created":"2017-03-19","cited_by_count":37},{"id":"1910.00694","title":"RITnet: Real-time Semantic Segmentation of the Eye for Gaze Tracking","created":"2019-10-01","cited_by_count":33},{"id":"2010.08705","title":"DEAL: Difficulty-aware Active Learning for Semantic Segmentation","created":"2020-10-17","cited_by_count":32},{"id":"2104.06394","title":"All you need are a few pixels: semantic segmentation with PixelPick","created":"2021-04-13","cited_by_count":31},{"id":"2010.02418","title":"The Effectiveness of Memory Replay in Large Scale Continual Learning","created":"2020-10-06","cited_by_count":26},{"id":"2010.01884","title":"MetaBox+: A new Region Based Active Learning Method for Semantic Segmentation using Priority Maps","created":"2020-10-05","cited_by_count":23},{"id":"2008.01860","title":"Importance of Self-Consistency in Active Learning for Semantic Segmentation","created":"2020-08-04","cited_by_count":21},{"id":"1908.02454","title":"An Adaptive Supervision Framework for Active Learning in Object Detection","created":"2019-08-07","cited_by_count":20},{"id":"2012.10467","title":"Minimax Active Learning","created":"2020-12-18","cited_by_count":18},{"id":"1912.08178","title":"AeroRIT: A New Scene for Hyperspectral Image Analysis","created":"2019-12-17","cited_by_count":10},{"id":"2007.06364","title":"On uncertainty estimation in active learning for image segmentation","created":"2020-07-13","cited_by_count":9}],"next_cursor":null}