AI RESEARCH
Refining Context-Entangled Content Segmentation via Curriculum Selection and Anti-Curriculum Promotion
arXiv CS.LG
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ArXi:2602.01183v2 Announce Type: replace-cross Biological learning proceeds from easy to difficult tasks, gradually reinforcing perception and robustness. Inspired by this principle, we address Context-Entangled Content Segmentation (CECS), a challenging setting where objects share intrinsic visual patterns with their surroundings, as in camouflaged object detection. Conventional segmentation networks predominantly rely on architectural enhancements but often ignore the learning dynamics that govern robustness under entangled data distributions. We.