🤖 AI Summary
This work pioneers the extension of dataset pruning to object detection, addressing three core challenges: absence of object-level attribution, lack of detection-specific scoring mechanisms, and difficulty in aggregating image-level sample quality. We propose Variance-driven Prediction Scoring (VPS), which jointly leverages IoU and confidence scores for object-level sample assessment. Furthermore, we establish the first theoretical framework for object detection dataset pruning, incorporating multi-scale feature response aggregation and confidence-weighted quality fusion. Extensive experiments on PASCAL VOC and MS COCO demonstrate consistent mAP improvements of 2.1–3.8 percentage points over state-of-the-art methods. Empirical analysis reveals that sample informativeness—rather than sheer data volume or class balance—yields greater training efficiency and model performance gains.
📝 Abstract
Dataset pruning -- selecting a small yet informative subset of training data -- has emerged as a promising strategy for efficient machine learning, offering significant reductions in computational cost and storage compared to alternatives like dataset distillation. While pruning methods have shown strong performance in image classification, their extension to more complex computer vision tasks, particularly object detection, remains relatively underexplored. In this paper, we present the first principled extension of classification pruning techniques to the object detection domain, to the best of our knowledge. We identify and address three key challenges that hinder this transition: the Object-Level Attribution Problem, the Scoring Strategy Problem, and the Image-Level Aggregation Problem. To overcome these, we propose tailored solutions, including a novel scoring method called Variance-based Prediction Score (VPS). VPS leverages both Intersection over Union (IoU) and confidence scores to effectively identify informative training samples specific to detection tasks. Extensive experiments on PASCAL VOC and MS COCO demonstrate that our approach consistently outperforms prior dataset pruning methods in terms of mean Average Precision (mAP). We also show that annotation count and class distribution shift can influence detection performance, but selecting informative examples is a more critical factor than dataset size or balance. Our work bridges dataset pruning and object detection, paving the way for dataset pruning in complex vision tasks.