{"list":"other","total":57,"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":"1409.1556","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","created":"2014-09-04","cited_by_count":15364},{"id":"2103.14030","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","created":"2021-03-25","cited_by_count":12139},{"id":"1512.00567","title":"Rethinking the Inception Architecture for Computer Vision","created":"2015-12-02","cited_by_count":5475},{"id":"1710.09412","title":"mixup: Beyond Empirical Risk Minimization","created":"2017-10-25","cited_by_count":5183},{"id":"1706.04599","title":"On Calibration of Modern Neural Networks","created":"2017-06-14","cited_by_count":4960},{"id":"1411.4038","title":"Fully Convolutional Networks for Semantic Segmentation","created":"2014-11-14","cited_by_count":4859},{"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":"1311.3618","title":"Describing Textures in the Wild","created":"2013-11-14","cited_by_count":2125},{"id":"1906.02530","title":"Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift","created":"2019-06-06","cited_by_count":1287},{"id":"1906.02629","title":"When Does Label Smoothing Help?","created":"2019-06-06","cited_by_count":988},{"id":"1701.06548","title":"Regularizing Neural Networks by Penalizing Confident Output Distributions","created":"2017-01-23","cited_by_count":464},{"id":"1805.10421","title":"Enhanced-alignment Measure for Binary Foreground Map Evaluation","created":"2018-05-26","cited_by_count":370},{"id":"1910.12656","title":"Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration","created":"2019-10-28","cited_by_count":364},{"id":"2002.09437","title":"Calibrating Deep Neural Networks using Focal Loss","created":"2020-02-21","cited_by_count":357},{"id":"1909.10155","title":"Verified Uncertainty Calibration","created":"2019-09-23","cited_by_count":309},{"id":"1905.11001","title":"On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks","created":"2019-05-27","cited_by_count":305},{"id":"1908.08297","title":"EGNet:Edge Guidance Network for Salient Object Detection","created":"2019-08-22","cited_by_count":237},{"id":"2105.09451","title":"Anabranch Network for Camouflaged Object Segmentation","created":"2021-05-20","cited_by_count":227},{"id":"1904.08739","title":"Cascaded Partial Decoder for Fast and Accurate Salient Object Detection","created":"2019-04-18","cited_by_count":216},{"id":"2007.09062","title":"Multi-scale Interactive Network for Salient Object Detection","created":"2020-07-17","cited_by_count":212},{"id":"2003.02819","title":"Does label smoothing mitigate label noise?","created":"2020-03-05","cited_by_count":204},{"id":"1406.2807","title":"The Secrets of Salient Object Segmentation","created":"2014-06-11","cited_by_count":200},{"id":"2103.04011","title":"Simultaneously Localize, Segment and Rank the Camouflaged Objects","created":"2021-03-06","cited_by_count":198},{"id":"2003.07329","title":"Mix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning","created":"2020-03-16","cited_by_count":190},{"id":"1503.08663","title":"Visual Saliency Based on Multiscale Deep Features","created":"2015-03-30","cited_by_count":185},{"id":"1911.11445","title":"F3Net: Fusion, Feedback and Focus for Salient Object Detection","created":"2019-11-26","cited_by_count":176},{"id":"2101.07663","title":"Salient Object Detection via Integrity Learning","created":"2021-01-19","cited_by_count":133},{"id":"1903.00179","title":"Pyramid Feature Attention Network for Saliency detection","created":"2019-03-01","cited_by_count":128},{"id":"2007.08074","title":"Suppress and Balance: A Simple Gated Network for Salient Object Detection","created":"2020-07-16","cited_by_count":126},{"id":"1807.09940","title":"Reverse Attention for Salient Object Detection","created":"2018-07-26","cited_by_count":113},{"id":"2008.11048","title":"Label Decoupling Framework for Salient Object Detection","created":"2020-08-25","cited_by_count":111},{"id":"2003.00651","title":"Global Context-Aware Progressive Aggregation Network for Salient Object Detection","created":"2020-03-02","cited_by_count":109},{"id":"1912.05170","title":"Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey","created":"2019-12-11","cited_by_count":101},{"id":"2012.08668","title":"Mitigating Bias in Calibration Error Estimation","created":"2020-12-15","cited_by_count":101},{"id":"1411.5878","title":"Salient Object Detection: A Survey","created":"2014-11-18","cited_by_count":98},{"id":"2003.07685","title":"Weakly-Supervised Salient Object Detection via Scribble Annotations","created":"2020-03-17","cited_by_count":96},{"id":"2011.12562","title":"Delving Deep into Label Smoothing","created":"2020-11-25","cited_by_count":94},{"id":"1708.02001","title":"Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection","created":"2017-08-07","cited_by_count":93},{"id":"2108.00106","title":"Soft Calibration Objectives for Neural Networks","created":"2021-07-30","cited_by_count":92},{"id":"1410.5926","title":"Salient Object Detection: A Discriminative Regional Feature Integration Approach","created":"2014-10-22","cited_by_count":89},{"id":"2003.05643","title":"Highly Efficient Salient Object Detection with 100K Parameters","created":"2020-03-12","cited_by_count":79},{"id":"1508.05154","title":"Posterior calibration and exploratory analysis for natural language processing models","created":"2015-08-21","cited_by_count":76},{"id":"2012.13093","title":"EDN: Salient Object Detection via Extremely-Downsampled Network","created":"2020-12-24","cited_by_count":64},{"id":"2112.13528","title":"Learning Generative Vision Transformer with Energy-Based Latent Space for Saliency Prediction","created":"2021-12-27","cited_by_count":57},{"id":"2009.03075","title":"Uncertainty Inspired RGB-D Saliency Detection","created":"2020-09-07","cited_by_count":56},{"id":"1605.00055","title":"DisturbLabel: Regularizing CNN on the Loss Layer","created":"2016-04-30","cited_by_count":41},{"id":"2206.02757","title":"Robust Calibration with Multi-domain Temperature Scaling","created":"2022-06-06","cited_by_count":39},{"id":"1704.03604","title":"Instance-Level Salient Object Segmentation","created":"2017-04-12","cited_by_count":38},{"id":"2108.03551","title":"Disentangled High Quality Salient Object Detection","created":"2021-08-08","cited_by_count":35},{"id":"2004.08595","title":"Dynamic Feature Integration for Simultaneous Detection of Salient Object, Edge and Skeleton","created":"2020-04-18","cited_by_count":34},{"id":"2211.11838","title":"AdaFocal: Calibration-aware Adaptive Focal Loss","created":"2022-11-21","cited_by_count":32},{"id":"2007.12211","title":"Learning Noise-Aware Encoder-Decoder from Noisy Labels by Alternating Back-Propagation for Saliency Detection","created":"2020-07-23","cited_by_count":29},{"id":"2006.11653","title":"Towards Understanding Label Smoothing","created":"2020-06-20","cited_by_count":17},{"id":"1903.10152","title":"SAC-Net: Spatial Attenuation Context for Salient Object Detection","created":"2019-03-25","cited_by_count":15},{"id":"2303.00900","title":"Transmission-Guided Bayesian Generative Model for Smoke Segmentation","created":"2023-03-02","cited_by_count":6}],"next_cursor":null}