{"list":"other","total":66,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"id":"1409.0575","title":"ImageNet Large Scale Visual Recognition Challenge","created":"2014-09-01","cited_by_count":8498},{"id":"2304.07193","title":"DINOv2: Learning Robust Visual Features without Supervision","created":"2023-04-14","cited_by_count":7974},{"id":"1602.05629","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","created":"2016-02-17","cited_by_count":7669},{"id":"2204.06125","title":"Hierarchical Text-Conditional Image Generation with CLIP Latents","created":"2022-04-13","cited_by_count":6585},{"id":"1710.09412","title":"mixup: Beyond Empirical Risk Minimization","created":"2017-10-25","cited_by_count":5183},{"id":"1812.06127","title":"Federated Optimization in Heterogeneous Networks","created":"2018-12-14","cited_by_count":2987},{"id":"1905.04899","title":"CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features","created":"2019-05-13","cited_by_count":2884},{"id":"2109.01134","title":"Learning to Prompt for Vision-Language Models","created":"2021-09-02","cited_by_count":2281},{"id":"1901.05555","title":"Class-Balanced Loss Based on Effective Number of Samples","created":"2019-01-16","cited_by_count":1245},{"id":"1706.07522","title":"Deep Hashing Network for Unsupervised Domain Adaptation","created":"2017-06-22","cited_by_count":1188},{"id":"2003.00295","title":"Adaptive Federated Optimization","created":"2020-02-29","cited_by_count":1046},{"id":"2110.04544","title":"CLIP-Adapter: Better Vision-Language Models with Feature Adapters","created":"2021-10-09","cited_by_count":1026},{"id":"1710.03077","title":"Deeper, Broader and Artier Domain Generalization","created":"2017-10-09","cited_by_count":986},{"id":"2007.07481","title":"Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization","created":"2020-07-15","cited_by_count":906},{"id":"2304.00685","title":"Vision-Language Models for Vision Tasks: A Survey","created":"2023-04-03","cited_by_count":839},{"id":"1910.09217","title":"Decoupling Representation and Classifier for Long-Tailed Recognition","created":"2019-10-21","cited_by_count":809},{"id":"2103.16257","title":"Model-Contrastive Federated Learning","created":"2021-03-30","cited_by_count":766},{"id":"1904.05160","title":"Large-Scale Long-Tailed Recognition in an Open World","created":"2019-04-10","cited_by_count":705},{"id":"2111.04263","title":"Federated Learning Based on Dynamic Regularization","created":"2021-11-08","cited_by_count":562},{"id":"2110.04596","title":"Deep Long-Tailed Learning: A Survey","created":"2021-10-09","cited_by_count":417},{"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":"1905.10497","title":"Fair Resource Allocation in Federated Learning","created":"2019-05-25","cited_by_count":394},{"id":"2105.10056","title":"Data-Free Knowledge Distillation for Heterogeneous Federated Learning","created":"2021-05-20","cited_by_count":383},{"id":"2207.09519","title":"Tip-Adapter: Training-free Adaption of CLIP for Few-shot Classification","created":"2022-07-19","cited_by_count":362},{"id":"2105.00243","title":"FedProto: Federated Prototype Learning across Heterogeneous Clients","created":"2021-05-01","cited_by_count":352},{"id":"2010.01809","title":"Long-tailed Recognition by Routing Diverse Distribution-Aware Experts","created":"2020-10-05","cited_by_count":271},{"id":"2106.05001","title":"No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data","created":"2021-06-09","cited_by_count":241},{"id":"2104.00466","title":"Improving Calibration for Long-Tailed Recognition","created":"2021-04-01","cited_by_count":212},{"id":"1910.06378","title":"SCAFFOLD: Stochastic Controlled Averaging for Federated Learning","created":"2019-10-14","cited_by_count":175},{"id":"2008.03673","title":"Feature Space Augmentation for Long-Tailed Data","created":"2020-08-09","cited_by_count":136},{"id":"2209.00189","title":"Federated Learning with Label Distribution Skew via Logits Calibration","created":"2022-09-01","cited_by_count":109},{"id":"2107.00233","title":"FedMix: Approximation of Mixup under Mean Augmented Federated Learning","created":"2021-07-01","cited_by_count":108},{"id":"2203.14197","title":"Long-Tailed Recognition via Weight Balancing","created":"2022-03-27","cited_by_count":107},{"id":"2112.00412","title":"The Majority Can Help The Minority: Context-rich Minority Oversampling for Long-tailed Classification","created":"2021-12-01","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":"2311.06750","title":"Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark","created":"2023-11-12","cited_by_count":99},{"id":"2101.10633","title":"ResLT: Residual Learning for Long-tailed Recognition","created":"2021-01-26","cited_by_count":96},{"id":"2006.07228","title":"FedGAN: Federated Generative Adversarial Networks for Distributed Data","created":"2020-06-12","cited_by_count":90},{"id":"2106.03097","title":"Preservation of the Global Knowledge by Not-True Distillation in Federated Learning","created":"2021-06-06","cited_by_count":87},{"id":"2109.12273","title":"FedProc: Prototypical Contrastive Federated Learning on Non-IID data","created":"2021-09-25","cited_by_count":81},{"id":"2010.05273","title":"Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms","created":"2020-10-11","cited_by_count":78},{"id":"2305.11733","title":"Long-tailed Visual Recognition via Gaussian Clouded Logit Adjustment","created":"2023-05-19","cited_by_count":75},{"id":"2302.13485","title":"FedCLIP: Fast Generalization and Personalization for CLIP in Federated Learning","created":"2023-02-27","cited_by_count":70},{"id":"2210.01033","title":"LPT: Long-tailed Prompt Tuning for Image Classification","created":"2022-10-03","cited_by_count":69},{"id":"2302.10911","title":"Revisiting Weighted Aggregation in Federated Learning with Neural Networks","created":"2023-02-14","cited_by_count":58},{"id":"2303.10058","title":"No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier","created":"2023-03-17","cited_by_count":55},{"id":"2111.14745","title":"A Simple Long-Tailed Recognition Baseline via Vision-Language Model","created":"2021-11-29","cited_by_count":49},{"id":"1807.10108","title":"Effects of Degradations on Deep Neural Network Architectures","created":"2018-07-26","cited_by_count":48},{"id":"2505.16394","title":"Raw2Drive: Reinforcement Learning with Aligned World Models for End-to-End Autonomous Driving (in CARLA v2)","created":"2025-05-22","cited_by_count":47},{"id":"2210.00226","title":"Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning","created":"2022-10-01","cited_by_count":46},{"id":"2304.01457","title":"Exploring Vision-Language Models for Imbalanced Learning","created":"2023-04-04","cited_by_count":35},{"id":"2105.00131","title":"GistNet: a Geometric Structure Transfer Network for Long-Tailed Recognition","created":"2021-05-01","cited_by_count":31},{"id":"2405.16585","title":"Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity","created":"2024-05-26","cited_by_count":30},{"id":"2404.05145","title":"UniMix: Towards Domain Adaptive and Generalizable LiDAR Semantic Segmentation in Adverse Weather","created":"2024-04-08","cited_by_count":24},{"id":"2212.14613","title":"Delving into Semantic Scale Imbalance","created":"2022-12-30","cited_by_count":22},{"id":"2301.12995","title":"FedFA: Federated Feature Augmentation","created":"2023-01-30","cited_by_count":22},{"id":"2306.06963","title":"Feature Fusion from Head to Tail for Long-Tailed Visual Recognition","created":"2023-06-12","cited_by_count":20},{"id":"2503.06457","title":"Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning","created":"2025-03-09","cited_by_count":12},{"id":"2401.11436","title":"Geometric Prior Guided Feature Representation Learning for Long-Tailed Classification","created":"2024-01-21","cited_by_count":10},{"id":"2303.12307","title":"Predicting and Enhancing the Fairness of DNNs with the Curvature of Perceptual Manifolds","created":"2023-03-22","cited_by_count":9},{"id":"2311.06965","title":"Anchor Data Augmentation","created":"2023-11-12","cited_by_count":6},{"id":"2404.13859","title":"Unveiling and Mitigating Generalized Biases of DNNs through the Intrinsic Dimensions of Perceptual Manifolds","created":"2024-04-22","cited_by_count":6},{"id":"2502.03852","title":"Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount","created":"2025-02-06","cited_by_count":3},{"id":"2403.00250","title":"Rethinking Classifier Re-Training in Long-Tailed Recognition: A Simple Logits Retargeting Approach","created":"2024-03-01","cited_by_count":2}],"next_cursor":null}