{"list":"other","total":51,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"2010.11929","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","created":"2020-10-22","cited_by_count":30186},{"id":"2103.14030","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","created":"2021-03-25","cited_by_count":12139},{"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":"1409.0575","title":"ImageNet Large Scale Visual Recognition Challenge","created":"2014-09-01","cited_by_count":8498},{"id":"1610.02391","title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization","created":"2016-10-07","cited_by_count":6733},{"id":"1608.06993","title":"Densely Connected Convolutional Networks","created":"2016-08-25","cited_by_count":6720},{"id":"1705.07874","title":"A Unified Approach to Interpreting Model Predictions","created":"2017-05-22","cited_by_count":6555},{"id":"1905.11946","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","created":"2019-05-28","cited_by_count":6442},{"id":"1512.00567","title":"Rethinking the Inception Architecture for Computer Vision","created":"2015-12-02","cited_by_count":5475},{"id":"1612.00593","title":"PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation","created":"2016-12-02","cited_by_count":5248},{"id":"1602.04938","title":"\"Why Should I Trust You?\": Explaining the Predictions of Any Classifier","created":"2016-02-16","cited_by_count":5070},{"id":"1801.04381","title":"MobileNetV2: Inverted Residuals and Linear Bottlenecks","created":"2018-01-13","cited_by_count":4795},{"id":"2012.12877","title":"Training data-efficient image transformers & distillation through attention","created":"2020-12-23","cited_by_count":4586},{"id":"1703.01365","title":"Axiomatic Attribution for Deep Networks","created":"2017-03-04","cited_by_count":3384},{"id":"1802.02611","title":"Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation","created":"2018-02-07","cited_by_count":3223},{"id":"2106.08254","title":"BEiT: BERT Pre-Training of Image Transformers","created":"2021-06-15","cited_by_count":2375},{"id":"1311.2901","title":"Visualizing and Understanding Convolutional Networks","created":"2013-11-12","cited_by_count":2290},{"id":"1801.07829","title":"Dynamic Graph CNN for Learning on Point Clouds","created":"2018-01-24","cited_by_count":2223},{"id":"1511.06434","title":"Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks","created":"2015-11-19","cited_by_count":2054},{"id":"1901.05555","title":"Class-Balanced Loss Based on Effective Number of Samples","created":"2019-01-16","cited_by_count":1245},{"id":"1707.07012","title":"Learning Transferable Architectures for Scalable Image Recognition","created":"2017-07-21","cited_by_count":1101},{"id":"1611.08097","title":"Geometric deep learning: going beyond Euclidean data","created":"2016-11-24","cited_by_count":1048},{"id":"2103.14899","title":"CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification","created":"2021-03-27","cited_by_count":806},{"id":"2103.17239","title":"Going deeper with Image Transformers","created":"2021-03-31","cited_by_count":764},{"id":"1703.10593","title":"Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks","created":"2017-03-30","cited_by_count":707},{"id":"2001.05566","title":"Image Segmentation Using Deep Learning: A Survey","created":"2020-01-15","cited_by_count":660},{"id":"1904.05046","title":"Generalizing from a Few Examples: A Survey on Few-Shot Learning","created":"2019-04-10","cited_by_count":463},{"id":"2105.03404","title":"ResMLP: Feedforward networks for image classification with data-efficient training","created":"2021-05-07","cited_by_count":339},{"id":"2106.09681","title":"XCiT: Cross-Covariance Image Transformers","created":"2021-06-17","cited_by_count":338},{"id":"1802.07623","title":"Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives","created":"2018-02-21","cited_by_count":262},{"id":"1910.03667","title":"REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs","created":"2019-10-08","cited_by_count":228},{"id":"1903.10831","title":"Attention Based Glaucoma Detection: A Large-scale Database and CNN Model","created":"2019-03-26","cited_by_count":76},{"id":"1801.00926","title":"Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation","created":"2018-01-03","cited_by_count":73},{"id":"1805.07549","title":"Disc-aware Ensemble Network for Glaucoma Screening from Fundus Image","created":"2018-05-19","cited_by_count":39},{"id":"2107.02375","title":"SplitAVG: A heterogeneity-aware federated deep learning method for medical imaging","created":"2021-07-06","cited_by_count":19},{"id":"2007.14848","title":"Difficulty-aware Glaucoma Classification with Multi-Rater Consensus Modeling","created":"2020-07-29","cited_by_count":17},{"id":"2005.02258","title":"AGE Challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography","created":"2020-05-05","cited_by_count":15},{"id":"2010.03872","title":"Clinically Verified Hybrid Deep Learning System for Retinal Ganglion Cells Aware Grading of Glaucomatous Progression","created":"2020-10-08","cited_by_count":15},{"id":"2007.11355","title":"Leveraging Undiagnosed Data for Glaucoma Classification with Teacher-Student Learning","created":"2020-07-22","cited_by_count":12},{"id":"2005.14284","title":"Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning","created":"2020-05-28","cited_by_count":11},{"id":"1810.10343","title":"From Machine to Machine: An OCT-trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs","created":"2018-10-20","cited_by_count":9},{"id":"2103.11895","title":"Deep learning on fundus images detects glaucoma beyond the optic disc","created":"2021-03-22","cited_by_count":9},{"id":"1905.08720","title":"Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation","created":"2019-05-21","cited_by_count":8},{"id":"2204.06931","title":"Geometric Deep Learning to Identify the Critical 3D Structural Features of the Optic Nerve Head for Glaucoma Diagnosis","created":"2022-04-14","cited_by_count":6},{"id":"1912.08387","title":"Iterative and Adaptive Sampling with Spatial Attention for Black-Box Model Explanations","created":"2019-12-18","cited_by_count":5},{"id":"2106.13551","title":"Circumpapillary OCT-Focused Hybrid Learning for Glaucoma Grading Using Tailored Prototypical Neural Networks","created":"2021-06-25","cited_by_count":5},{"id":"2204.05591","title":"Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review","created":"2022-04-12","cited_by_count":5},{"id":"2110.15912","title":"On the use of uncertainty in classifying Aedes Albopictus mosquitoes","created":"2021-10-29","cited_by_count":4},{"id":"2204.07004","title":"Medical Application of Geometric Deep Learning for the Diagnosis of Glaucoma","created":"2022-04-14","cited_by_count":4},{"id":"1905.06509","title":"TRk-CNN: Transferable Ranking-CNN for image classification of glaucoma, glaucoma suspect, and normal eyes","created":"2019-05-16","cited_by_count":1}],"next_cursor":null}