Selectivity Drives Efficiency: Dataset Pruning for Visual Place Recognition

• URL: http://arxiv.org/abs/2607.14897v1
• Date: Thu, 16 Jul 2026 12:16:00 GMT
• Title: Selectivity Drives Efficiency: Dataset Pruning for Visual Place Recognition
• Abstract summary: We propose a place-wise dataset pruning framework tailored for visual place recognition (VPR)<n>Our method treats each place as the basic pruning unit and introduces two novel metrics, i.e., intra-place diversity (IPD) and inter-place similarity (IPS)<n>Experiments demonstrate that our method consistently outperforms state-of-the-art DP baselines under different pruning ratios.
• Score: 52.52131290631774
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Plug-and-Adapt: Multimodal Coreference Resolution at First Sight with a Pretrained Alignment Model [47.811446766453834]
  Existing Multi-modal Coreference Resolution (MCR) methods require training with annotated data from the target dataset before they can be applied.<n>We propose a plug-and-adapt method that strategically adapts a carefully pre-trained emphalignment model for immediate use in MCR tasks.
  arXiv  Detail & Related papers  (2026-06-16T14:04:13Z)
• Selecting Samples on Graphs: A Unified Dataset Pruning Framework for Lossless Training Acceleration [71.77541419885925]
  We present a unified graph-based DP framework.<n>By modeling the dataset as a weighted graph, DP can be cast as a Maximum Weight Clique Problem (MWCP)<n>Our method outperforms existing DP methods while substantially reducing training cost, reducing training time by over 40% without sacrificing accuracy on ImageNet-1k with ResNet-50.
  arXiv  Detail & Related papers  (2026-06-11T05:13:32Z)
• Quality over Quantity: An Effective Large-Scale Data Reduction Strategy Based on Pointwise V-Information [2.133855532092057]
  We propose an effective data reduction strategy based on Pointwise V-Information (PVI)<n>Experiments show that classifier performance is maintained with only a 0.0001% to 0.76% decline in accuracy when 10%-30% of the data is removed.<n>We have adapted the PVI framework, which was previously limited to English datasets, to a variety of Chinese Natural Language Processing (NLP) tasks and base models.
  arXiv  Detail & Related papers  (2025-06-19T06:59:19Z)
• SelaVPR++: Towards Seamless Adaptation of Foundation Models for Efficient Place Recognition [91.98099115144511]
  Recent studies show that the visual place recognition (VPR) method using pre-trained visual foundation models can achieve promising performance.<n>We propose a novel method to realize seamless adaptation of foundation models to VPR.<n>In pursuit of higher efficiency and better performance, we propose an extension of the SelaVPR, called SelaVPR++.
  arXiv  Detail & Related papers  (2025-02-23T15:01:09Z)
• SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency [10.555957282859]
  This paper introduces a novel dynamic bootstrapping dataset pruning method. It involves pruning data preparation followed by dataset mutation operations, both of which undergo iterative and dynamic updates. We individually pre-train seven CLIP models on two large-scale image-text pair datasets, and two MoCo models on the ImageNet dataset, resulting in a total of 16 pre-trained models.
  arXiv  Detail & Related papers  (2024-11-14T01:53:17Z)
• DANCE: Dual-View Distribution Alignment for Dataset Condensation [39.08022095906364]
  We propose a new DM-based method named Dual-view distribution AligNment for dataset CondEnsation (DANCE) Specifically, from the inner-class view, we construct multiple "middle encoders" to perform pseudo long-term distribution alignment. While from the inter-class view, we use the expert models to perform distribution calibration.
  arXiv  Detail & Related papers  (2024-06-03T07:22:17Z)
• EffoVPR: Effective Foundation Model Utilization for Visual Place Recognition [6.996304653818122]
  We present an effective approach to harness the potential of a foundation model for Visual Place Recognition.<n>We show that features extracted from self-attention layers can act as a powerful re-ranker for VPR, even in a zero-shot setting.<n>Our method also demonstrates exceptional robustness and generalization, setting new state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-05-28T11:24:41Z)
• Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition [72.35438297011176]
  We propose a novel method to realize seamless adaptation of pre-trained models for visual place recognition (VPR) Specifically, to obtain both global and local features that focus on salient landmarks for discriminating places, we design a hybrid adaptation method. Experimental results show that our method outperforms the state-of-the-art methods with less training data and training time.
  arXiv  Detail & Related papers  (2024-02-22T12:55:01Z)
• Few-Shot Named Entity Recognition: A Comprehensive Study [92.40991050806544]
  We investigate three schemes to improve the model generalization ability for few-shot settings. We perform empirical comparisons on 10 public NER datasets with various proportions of labeled data. We create new state-of-the-art results on both few-shot and training-free settings.
  arXiv  Detail & Related papers  (2020-12-29T23:43:16Z)
• Omni-supervised Facial Expression Recognition via Distilled Data [120.11782405714234]
  We propose omni-supervised learning to exploit reliable samples in a large amount of unlabeled data for network training. We experimentally verify that the new dataset can significantly improve the ability of the learned FER model. To tackle this, we propose to apply a dataset distillation strategy to compress the created dataset into several informative class-wise images.
  arXiv  Detail & Related papers  (2020-05-18T09:36:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.