{"list":"other","total":85,"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":"1405.0312","title":"Microsoft COCO: Common Objects in Context","created":"2014-05-01","cited_by_count":17608},{"id":"1707.06347","title":"Proximal Policy Optimization Algorithms","created":"2017-07-20","cited_by_count":15215},{"id":"2402.03300","title":"DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models","created":"2024-02-05","cited_by_count":9379},{"id":"1409.0575","title":"ImageNet Large Scale Visual Recognition Challenge","created":"2014-09-01","cited_by_count":8498},{"id":"2304.02643","title":"Segment Anything","created":"2023-04-05","cited_by_count":8234},{"id":"1502.03167","title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","created":"2015-02-11","cited_by_count":7005},{"id":"1607.06450","title":"Layer Normalization","created":"2016-07-21","cited_by_count":5669},{"id":"1905.04899","title":"CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features","created":"2019-05-13","cited_by_count":2884},{"id":"2408.00714","title":"SAM 2: Segment Anything in Images and Videos","created":"2024-08-01","cited_by_count":2863},{"id":"1807.06521","title":"CBAM: Convolutional Block Attention Module","created":"2018-07-17","cited_by_count":2477},{"id":"1512.04150","title":"Learning Deep Features for Discriminative Localization","created":"2015-12-14","cited_by_count":2436},{"id":"2004.07780","title":"Shortcut Learning in Deep Neural Networks","created":"2020-04-16","cited_by_count":1969},{"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":"1901.05555","title":"Class-Balanced Loss Based on Effective Number of Samples","created":"2019-01-16","cited_by_count":1245},{"id":"1905.13549","title":"Learning Robust Global Representations by Penalizing Local Predictive Power","created":"2019-05-29","cited_by_count":1013},{"id":"1906.07413","title":"Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss","created":"2019-06-18","cited_by_count":989},{"id":"1904.05160","title":"Large-Scale Long-Tailed Recognition in an Open World","created":"2019-04-10","cited_by_count":705},{"id":"2006.08218","title":"Self-supervised Learning: Generative or Contrastive","created":"2020-06-15","cited_by_count":694},{"id":"2007.07314","title":"Long-tail learning via logit adjustment","created":"2020-07-14","cited_by_count":618},{"id":"2011.00362","title":"A Survey on Contrastive Self-supervised Learning","created":"2020-10-31","cited_by_count":554},{"id":"2110.04596","title":"Deep Long-Tailed Learning: A Survey","created":"2021-10-09","cited_by_count":417},{"id":"1905.13655","title":"Implicit Regularization in Deep Matrix Factorization","created":"2019-05-31","cited_by_count":405},{"id":"1912.02413","title":"BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition","created":"2019-12-05","cited_by_count":403},{"id":"2007.10740","title":"Balanced Meta-Softmax for Long-Tailed Visual Recognition","created":"2020-07-21","cited_by_count":400},{"id":"2006.09994","title":"Noise or Signal: The Role of Image Backgrounds in Object Recognition","created":"2020-06-17","cited_by_count":343},{"id":"2010.01809","title":"Long-tailed Recognition by Routing Diverse Distribution-Aware Experts","created":"2020-10-05","cited_by_count":271},{"id":"2009.06962","title":"Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup","created":"2020-09-15","cited_by_count":261},{"id":"2009.12991","title":"Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect","created":"2020-09-28","cited_by_count":256},{"id":"1905.00397","title":"Fast AutoAugment","created":"2019-05-01","cited_by_count":243},{"id":"2103.02907","title":"Coordinate Attention for Efficient Mobile Network Design","created":"2021-03-04","cited_by_count":241},{"id":"2002.09434","title":"Few-Shot Learning via Learning the Representation, Provably","created":"2020-02-21","cited_by_count":214},{"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":"1807.06514","title":"BAM: Bottleneck Attention Module","created":"2018-07-17","cited_by_count":172},{"id":"2012.00321","title":"Disentangling Label Distribution for Long-tailed Visual Recognition","created":"2020-12-01","cited_by_count":172},{"id":"2006.01791","title":"SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization","created":"2020-06-02","cited_by_count":149},{"id":"2207.09052","title":"Balanced Contrastive Learning for Long-Tailed Visual Recognition","created":"2022-07-19","cited_by_count":144},{"id":"2103.14267","title":"Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification","created":"2021-03-26","cited_by_count":130},{"id":"2302.13007","title":"AugGPT: Leveraging ChatGPT for Text Data Augmentation","created":"2023-02-25","cited_by_count":130},{"id":"1904.02874","title":"An Attentive Survey of Attention Models","created":"2019-04-05","cited_by_count":124},{"id":"2010.05300","title":"Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification","created":"2020-10-11","cited_by_count":100},{"id":"2103.12579","title":"MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition","created":"2021-03-23","cited_by_count":99},{"id":"1911.06987","title":"Faster AutoAugment: Learning Augmentation Strategies using Backpropagation","created":"2019-11-16","cited_by_count":96},{"id":"2107.09249","title":"Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition","created":"2021-07-20","cited_by_count":95},{"id":"2104.03066","title":"Distributional Robustness Loss for Long-tail Learning","created":"2021-04-07","cited_by_count":84},{"id":"2305.11733","title":"Long-tailed Visual Recognition via Gaussian Clouded Logit Adjustment","created":"2023-05-19","cited_by_count":75},{"id":"2012.11101","title":"ResizeMix: Mixing Data with Preserved Object Information and True Labels","created":"2020-12-21","cited_by_count":69},{"id":"2109.03216","title":"Learning Fast Sample Re-weighting Without Reward Data","created":"2021-09-07","cited_by_count":69},{"id":"1712.03162","title":"Class Rectification Hard Mining for Imbalanced Deep Learning","created":"2017-12-08","cited_by_count":67},{"id":"2301.11104","title":"Discovering and Mitigating Visual Biases through Keyword Explanation","created":"2023-01-26","cited_by_count":58},{"id":"2012.04846","title":"SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data","created":"2020-12-09","cited_by_count":56},{"id":"1812.06707","title":"Not Using the Car to See the Sidewalk: Quantifying and Controlling the Effects of Context in Classification and Segmentation","created":"2018-12-17","cited_by_count":49},{"id":"2102.05228","title":"Towards Better Explanations of Class Activation Mapping","created":"2021-02-10","cited_by_count":47},{"id":"2002.08822","title":"Automatic Shortcut Removal for Self-Supervised Representation Learning","created":"2020-02-20","cited_by_count":43},{"id":"2310.18236","title":"How Re-sampling Helps for Long-Tail Learning?","created":"2023-10-27","cited_by_count":36},{"id":"2108.00049","title":"Object-aware Contrastive Learning for Debiased Scene Representation","created":"2021-07-30","cited_by_count":35},{"id":"2007.09898","title":"Solving Long-tailed Recognition with Deep Realistic Taxonomic Classifier","created":"2020-07-20","cited_by_count":34},{"id":"2403.05854","title":"LTGC: Long-tail Recognition via Leveraging LLMs-driven Generated Content","created":"2024-03-09","cited_by_count":33},{"id":"2304.14299","title":"A Probabilistic Attention Model with Occlusion-aware Texture Regression for 3D Hand Reconstruction from a Single RGB Image","created":"2023-04-27","cited_by_count":32},{"id":"2105.00131","title":"GistNet: a Geometric Structure Transfer Network for Long-Tailed Recognition","created":"2021-05-01","cited_by_count":31},{"id":"2505.22483","title":"A Closer Look at Multimodal Representation Collapse","created":"2025-05-28","cited_by_count":28},{"id":"2211.12883","title":"Mitigating and Evaluating Static Bias of Action Representations in the Background and the Foreground","created":"2022-11-23","cited_by_count":27},{"id":"2306.15925","title":"Subclass-balancing Contrastive Learning for Long-tailed Recognition","created":"2023-06-28","cited_by_count":26},{"id":"2012.09392","title":"MASKER: Masked Keyword Regularization for Reliable Text Classification","created":"2020-12-17","cited_by_count":25},{"id":"2302.05011","title":"Context Understanding in Computer Vision: A Survey","created":"2023-02-10","cited_by_count":23},{"id":"2306.06963","title":"Feature Fusion from Head to Tail for Long-Tailed Visual Recognition","created":"2023-06-12","cited_by_count":20},{"id":"2308.09922","title":"MDCS: More Diverse Experts with Consistency Self-distillation for Long-tailed Recognition","created":"2023-08-19","cited_by_count":19},{"id":"2412.08647","title":"SegFace: Face Segmentation of Long-Tail Classes","created":"2024-12-11","cited_by_count":18},{"id":"2404.17642","title":"Empowering Large Language Models for Textual Data Augmentation","created":"2024-04-26","cited_by_count":17},{"id":"2111.04051","title":"Coordinated Proximal Policy Optimization","created":"2021-11-07","cited_by_count":16},{"id":"2602.22394","title":"Vision Transformers Need More Than Registers","created":"2026-02-25","cited_by_count":15},{"id":"2404.02900","title":"DeiT-LT Distillation Strikes Back for Vision Transformer Training on Long-Tailed Datasets","created":"2024-04-03","cited_by_count":14},{"id":"2505.01660","title":"Focal-SAM: Focal Sharpness-Aware Minimization for Long-Tailed Classification","created":"2025-05-03","cited_by_count":14},{"id":"2410.15980","title":"Learning from Neighbors: Category Extrapolation for Long-Tail Learning","created":"2024-10-21","cited_by_count":9},{"id":"2503.12821","title":"From Head to Tail: Towards Balanced Representation in Large Vision-Language Models through Adaptive Data Calibration","created":"2025-03-17","cited_by_count":8},{"id":"2407.04911","title":"Enhanced Long-Tailed Recognition with Contrastive CutMix Augmentation","created":"2024-07-06","cited_by_count":7},{"id":"2502.11671","title":"Diversity-oriented Data Augmentation with Large Language Models","created":"2025-02-17","cited_by_count":7},{"id":"2409.18055","title":"Visual Data Diagnosis and Debiasing with Concept Graphs","created":"2024-09-26","cited_by_count":6},{"id":"2401.15885","title":"Rectify the Regression Bias in Long-Tailed Object Detection","created":"2024-01-29","cited_by_count":5},{"id":"2507.05193","title":"RAM-W600: A Multi-Task Wrist Dataset and Benchmark for Rheumatoid Arthritis","created":"2025-07-07","cited_by_count":5},{"id":"2310.20490","title":"Long-Tailed Learning as Multi-Objective Optimization","created":"2023-10-31","cited_by_count":4},{"id":"2505.15123","title":"Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding","created":"2025-05-21","cited_by_count":3},{"id":"2507.19131","title":"MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective","created":"2025-07-25","cited_by_count":3},{"id":"2603.21217","title":"Reframing Long-Tailed Learning via Loss Landscape Geometry","created":"2026-03-22","cited_by_count":1}],"next_cursor":null}