2 papers
cs.CV2026
Debiased Orthogonal Boundary-Driven Efficient Noise Mitigation
Hao Li, Jiayang Gu, Jingkuan Song +2
Mitigating the detrimental effects of noisy labels on the training process has become increasingly critical, as obtaining entirely clean or human-annotated samples for large-scale…
cs.CV2025
Exponentially Weighted Instance-Aware Repeat Factor Sampling for Long-Tailed Object Detection Model Training in Unmanned Aerial Vehicles Surveillance Scenarios
Taufiq Ahmed, Abhishek Kumar, Constantino Ãlvarez Casado +5
Object detection models often struggle with class imbalance, where rare categories appear significantly less frequently than common ones. Existing sampling-based rebalancing strate…