面向上下文依赖分割的标注高效核心集选择
Annotation-Efficient Coreset Selection for Context-dependent Segmentation
把 coreset 选择建模为前景/背景分布间的最优传输问题，并用基于点的破坏-重建过程估计传输代价，这套「弱标注打分 + 最大距离熵采样」可直接迁移到分割/检测数据集的高效标注与主动学习流程中。
原文依据 we formulate sample evaluation as an optimal transport problem between foreground and background distributions
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论上下文依赖（CD）任务要求模型具备高级视觉理解能力，例如识别伪装目标和医学病灶。当前的 CD 方法严重依赖像素级标注的训练集，忽视了冗余样本问题与高昂的标注成本。本文针对 CD 数据集的剪枝需求，聚焦于利用弱标注挑选最有价值的样本进行标注与训练。为此，我们将 CD 核心集选择分解为样本评估与核心集选择两个步骤，并提出相应解决方案：基于点的最优传输与最大距离熵策略。具体而言，我们将样本评估建模为前景与背景分布之间的最优传输问题，设计基于点的前景破坏-重建过程以计算传输代价并对样本打分。对于重要性各异的样本，我们的选择策略在核心集覆盖度与多样性之间取得平衡。我们在六个 CD 任务上验证了该方法，在 40% 剪枝率下相对全量训练仅损失 1% 的精度。
英文原文摘要 1134 字符
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.