Annotation-Efficient Coreset Selection for Context-dependent Segmentation

Context-dependent (CD) tasks demand the model to have advanced visual understanding ability, such as recognizing camouflaged objects and medical lesions. Current CD methods rely heavily on pixel-level annotated training sets, neglecting issues from redundant samples and the high annotation costs. In this paper, we address the pruning needs of CD datasets, focusing on selecting the most valuable samples for labeling and training using weak annotations. To achieve this, we decompose CD coreset selection into two steps: sample evaluation and coreset selection, proposing corresponding solutions: points-based optimal transport and a maximum distance entropy strategy. Specifically, we formulate sample evaluation as an optimal transport problem between foreground and background distributions, designing a foreground destruction-reconstruction process based on points to compute transport costs and score samples. For samples of varying importance, our selection strategy balances coreset coverage and diversity. We validate our method on six CD tasks, achieving 1% accuracy loss relative to full training under a 40% pruning rate.