Faster or Stronger: Towards Flexible Visual Place Recognition via Weighted Aggregation and Token Pruning
Visual Place Recognition (VPR) aims to match a query image to reference images of the same place in a large-scale database. Recent state-of-the-art methods employ Vision Transformers (ViTs) as backbone foundation models to extract patch-level features that are robust to viewpoint, illumination, and seasonal variations, which are then aggregated into a compact global descriptor for retrieval. Most existing aggregation methods uniformly pool patch tokens into learned clusters, despite the fact that different clusters often encode distinct spatial or semantic patterns and contribute unequally to VPR performance. To address this limitation, we propose Weighted Aggregated Descriptor (WeiAD), w...
| Authors | Zichao Zeng, June Moh Goo, Junwei Zheng et al. |
|---|---|
| Primary category | cs.CV |
| Published | 19 May 2026 |
| Last updated at source | 19 May 2026 |
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