{"total":18,"rows":[{"id":"2403.11868","title":"View-Consistent 3D Editing with Gaussian Splatting","created":"2024-03-18","cited_by_count":67,"area":null},{"id":"2305.05230","title":"FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity","created":"2023-05-09","cited_by_count":33,"area":null},{"id":"2404.04562","title":"Diffusion Time-step Curriculum for One Image to 3D Generation","created":"2024-04-06","cited_by_count":21,"area":null},{"id":"2208.09833","title":"Label-Noise Learning with Intrinsically Long-Tailed Data","created":"2022-08-21","cited_by_count":17,"area":null},{"id":"2310.16002","title":"Integrating View Conditions for Image Synthesis","created":"2023-10-24","cited_by_count":16,"area":null},{"id":"2308.09694","title":"Invariant Training 2D-3D Joint Hard Samples for Few-Shot Point Cloud Recognition","created":"2023-08-18","cited_by_count":13,"area":null},{"id":"2308.03282","title":"Environment-Invariant Curriculum Relation Learning for Fine-Grained Scene Graph Generation","created":"2023-08-07","cited_by_count":9,"area":null},{"id":"2211.08722","title":"Learning with Noisy Labels over Imbalanced Subpopulations","created":"2022-11-16","cited_by_count":5,"area":null},{"id":"2502.08227","title":"Enhancing Sample Selection Against Label Noise by Cutting Mislabeled Easy Examples","created":"2025-02-12","cited_by_count":4,"area":null},{"id":"2502.16129","title":"Robust Dynamic Facial Expression Recognition","created":"2025-02-22","cited_by_count":4,"area":null},{"id":"2404.06795","title":"Extracting Clean and Balanced Subset for Noisy Long-tailed Classification","created":"2024-04-10","cited_by_count":3,"area":null},{"id":"2211.10906","title":"Learning from Long-Tailed Noisy Data with Sample Selection and Balanced Loss","created":"2022-11-20","cited_by_count":2,"area":null},{"id":"2311.01744","title":"Data-Centric Long-Tailed Image Recognition","created":"2023-11-03","cited_by_count":1,"area":null},{"id":"2510.08179","title":"Dual-granularity Sinkhorn Distillation for Enhanced Learning from Long-tailed Noisy Data","created":"2025-10-09","cited_by_count":1,"area":null},{"id":"2605.23254","title":"CARE: Class-Adaptive Expert Consensus for Reliable Learning with Long-Tailed Noisy Labels","created":"2026-05-22","cited_by_count":1,"area":null},{"id":"2403.02363","title":"Addressing Long-Tail Noisy Label Learning Problems: a Two-Stage Solution with Label Refurbishment Considering Label Rarity","created":"2024-03-04","cited_by_count":0,"area":null},{"id":"2604.23125","title":"Learning from Imperfect Text Guidance: Robust Long-Tail Visual Recognition with High-Noise Label","created":"2026-04-25","cited_by_count":0,"area":null},{"id":"2608.00678","title":"Breaking the Horizontal Prior: From Long-Tailed Orientation Bias to Roll-Robust Monocular Depth Estimation","created":"2026-08-01","cited_by_count":0,"area":null}],"next_cursor":null}