In this paper, we propose a foreground-aware dataset distillation method that enhances patch selection in a content-adaptive manner. With the rising computational cost of training large-scale deep models, dataset distillation has emerged as a promising approach for constructing compact synthetic datasets that retain the knowledge of their large original counterparts. However, traditional optimization-based methods often suffer from high computational overhead, memory constraints, and the generation of unrealistic, noise-like images with limited architectural generalization. Recent non-optimization methods alleviate some of these issues by constructing distilled data from real image patches, but the used rigid patch selection strategies can still discard critical information about the main objects. To solve this problem, we first leverage Grounded SAM2 to identify foreground objects and compute per-image foreground occupancy, from which we derive a category-wise patch decision threshold. Guided by these thresholds, we design a dynamic patch selection strategy that, for each image, either selects the most informative patch from multiple candidates or directly resizes the full image when the foreground dominates. This dual-path mechanism preserves more key information about the main objects while reducing redundant background content. Extensive experiments on multiple benchmarks show that the proposed method consistently improves distillation performance over existing approaches, producing more informative and representative distilled datasets and enhancing robustness across different architectures and image compositions.

翻译：本文提出一种前景感知的数据集蒸馏方法，以内容自适应方式增强补丁选择。随着大规模深度模型训练计算成本的不断攀升，数据集蒸馏已成为构建保留原始大规模数据集知识的紧凑合成数据集的有效途径。然而，传统的基于优化的方法常受限于高计算开销、内存约束，且生成的图像往往呈现不真实的噪声状特征，架构泛化能力有限。近期非优化方法通过从真实图像补丁构建蒸馏数据缓解了部分问题，但采用的刚性补丁选择策略仍可能丢弃主体对象的关键信息。为解决此问题，我们首先利用Grounded SAM2识别前景对象并计算单图像前景占比，据此推导出类别级补丁决策阈值。在这些阈值指导下，我们设计了一种动态补丁选择策略：针对每幅图像，当前景占主导时直接缩放完整图像，否则从多个候选补丁中选择信息量最大的补丁。这种双路径机制在减少冗余背景内容的同时，保留了更多关于主体对象的关键信息。在多基准测试上的大量实验表明，所提方法相较于现有方法持续提升蒸馏性能，生成信息更丰富、代表性更强的蒸馏数据集，并增强了跨不同架构与图像构成的鲁棒性。