← 研踪 PaperTrailCVF · CVPR 2026:cvf:ad6db97f6e99c83e9c7fc7ee8f1a80b3 · 记录 #1
Structure-Aware Representation Distillation for Tiny-Dense Object Segmentation
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 00:57
摘要
Dense scenes containing numerous tiny objects pose a fundamental challenge for segmentation models, where small localization errors can significantly degrade downstream measurements. We present Structure-Aware Representation Distillation (SARD), a teacher-compatible framework that transfers structural knowledge from a large teacher to a compact student via feature-space alignment rather than mask imitation. SARD builds a structure-importance map by integrating boundary salience, geometric complexity, and local feature variation, and uses it to guide a unified representation loss that combines feature consistency with distribution alignment. This encourages the student to allocate capacity to geometrically informative regions while preserving global context. Experiments on Cityscapes, ADE20K, and a challenging rock fragmentation benchmark (RockFrag) show that SARD consistently improves both mIoU and boundary IoU over strong distillation baselines; on RockFrag, SARD improves a Swin-T student over CWD by +4.3 mIoU and +6.7 bIoU. A ResNet-50 student distilled from a Swin-L teacher achieves up to 7.7 times parameter reduction and 9 times higher throughput than the teacher, with no additional inference overhead beyond the student network, demonstrating that structure-aware representation distillation is effective and efficient for tiny-dense segmentation. Code is available at: https://github.com/liuuuuuuxuesong/SARD.
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记录 #1 · 2026 · 最新观测版本
Structure-Aware Representation Distillation for Tiny-Dense Object Segmentation
Dense scenes containing numerous tiny objects pose a fundamental challenge for segmentation models, where small localization errors can significantly degrade downstream measurements. We present Structure-Aware Representation Distillation (SARD), a teacher-compatible framework that transfers structural knowledge from a large teacher to a compact student via feature-space alignment rather than mask imitation. SARD builds a structure-importance map by integrating boundary salience, geometric complexity, and local feature variation, and uses it to guide a unified representation loss that combines feature consistency with distribution alignment. This encourages the student to allocate capacity to geometrically informative regions while preserving global context. Experiments on Cityscapes, ADE20K, and a challenging rock fragmentation benchmark (RockFrag) show that SARD consistently improves both mIoU and boundary IoU over strong distillation baselines; on RockFrag, SARD improves a Swin-T student over CWD by +4.3 mIoU and +6.7 bIoU. A ResNet-50 student distilled from a Swin-L teacher achieves up to 7.7 times parameter reduction and 9 times higher throughput than the teacher, with no additional inference overhead beyond the student network, demonstrating that structure-aware representation distillation is effective and efficient for tiny-dense segmentation. Code is available at: https://github.com/liuuuuuuxuesong/SARD.
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