发表机构
Shenyang Institute of Automation, Chinese Academy of Sciences; University of Chinese Academy of Sciences; Tsinghua Shenzhen International Graduate School, Tsinghua University; Shenzhen University of Advanced Technology（中国科学院沈阳自动化研究所; 中国科学院大学; 清华大学深圳国际研究生院; 深圳先进技术研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对视觉场所识别中数据集存储和训练成本高的问题，提出场所级数据集剪枝框架，引入IPD和IPS指标评估场所训练价值，构建核心集，实验证明该方法能在不同剪枝率下降低成本并保持高识别性能。
AI 中文摘要
近期视觉场所识别（VPR）研究越来越依赖大规模数据集来训练更强大、更具判别力的模型。这虽提升了识别性能，但带来巨大存储和训练成本。数据集剪枝（DP）是提升数据效率的有效方式，但传统DP方法忽视了VPR中基于图像对的监督关系。为此，我们提出了专为VPR设计的场所级数据集剪枝框架。该方法将每个场所作为基本剪枝单元，引入场所内多样性（IPD）和场所间相似度（IPS）两个新指标评估场所训练价值，构建紧凑且信息丰富的核心集。实验表明，该方法在不同剪枝率下优于现有DP基线，降低了选择和训练成本。例如，将合并数据集剪枝后，在MSLS-val上R@1达到94.5%，在Nordland上达到97.0%。代码将公开。
英文摘要
Recent visual place recognition (VPR) studies have increasingly relied on large-scale datasets to train more robust and discriminative models. Although this trend significantly improves recognition performance, it also introduces substantial storage and training costs, especially when new architectures or training strategies need to be repeatedly developed and evaluated. Dataset pruning (DP) provides a promising way to improve data efficiency by retaining only informative training data. However, conventional DP methods mainly follow the sample-wise classification paradigm, which overlooks the relation-dependent training nature of VPR, where supervision is typically formed by image pairs rather than independent images. To address this issue, we propose a place-wise dataset pruning framework tailored for VPR. Instead of pruning individual images, our method treats each place as the basic pruning unit and introduces two complementary novel metrics, i.e., intra-place diversity (IPD) and inter-place similarity (IPS), to evaluate the training value of each place. By jointly considering these two metrics, our method ranks all places and constructs a compact yet informative coreset, thereby allowing the pruned dataset to still support the training of robust and discriminative VPR models. Extensive experiments demonstrate that our method consistently outperforms state-of-the-art DP baselines under different pruning ratios while reducing selection and training costs. Moreover, by pruning a merged dataset roughly 3.5$\times$ the size of GSV-Cities to a comparable scale, our coreset maintains highly competitive performance, achieving 94.5\% R@1 on MSLS-val and 97.0\% R@1 on Nordland with only NetVLAD. Codes will be made publicly available.
Comments14 pages,7 figures,15 tables
