Difficulty-Aware Sample Allocation for Adaptive Data Augmentation in Semantic Segmentation
Read the original on arXiv Computer Vision →The paper proposes Difficulty-Aware Sample Allocation (DASA), a framework that assigns stronger data augmentation to training samples deemed more difficult based on a composite difficulty score. This score integrates prediction ambiguity, training loss, class rarity, and boundary complexity, and is used to modulate augmentation strength during iterative training. Experiments on Oxford‑IIIT Pet and binary Pascal VOC with U‑Net, DeepLabV3, and SegFormer‑B0 demonstrate that DASA outperforms standard training and matches or exceeds single‑signal adaptive baselines, notably raising DeepLabV3’s mIoU from 0.633 to 0.740 on Oxford‑IIIT Pet.
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