Training-Free Dataset Pruning for Instance Segmentation

• URL: http://arxiv.org/abs/2503.00828v1
• Date: Sun, 02 Mar 2025 10:05:59 GMT
• Title: Training-Free Dataset Pruning for Instance Segmentation
• Authors: Yalun Dai, Lingao Xiao, Ivor W. Tsang, Yang He,
• Abstract summary: Instance segmentation presents three key challenges: pixel-level annotations, instance area variations, and class imbalances.<n>We propose a novel Training-Free dataset Pruning (TFDP) method for instance segmentation.<n>We achieve state-of-the-art results on VOC 2012, Cityscapes, and COCO datasets, generalizing well across CNN and Transformer architectures.
• Score: 35.124251909622025
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing dataset pruning techniques primarily focus on classification tasks, limiting their applicability to more complex and practical tasks like instance segmentation. Instance segmentation presents three key challenges: pixel-level annotations, instance area variations, and class imbalances, which significantly complicate dataset pruning efforts. Directly adapting existing classification-based pruning methods proves ineffective due to their reliance on time-consuming model training process. To address this, we propose a novel Training-Free Dataset Pruning (TFDP) method for instance segmentation. Specifically, we leverage shape and class information from image annotations to design a Shape Complexity Score (SCS), refining it into a Scale-Invariant (SI-SCS) and Class-Balanced (CB-SCS) versions to address instance area variations and class imbalances, all without requiring model training. We achieve state-of-the-art results on VOC 2012, Cityscapes, and COCO datasets, generalizing well across CNN and Transformer architectures. Remarkably, our approach accelerates the pruning process by an average of 1349$\times$ on COCO compared to the adapted baselines. Source code is available at: https://github.com/he-y/dataset-pruning-for-instance-segmentation

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