LaVPR: Benchmarking Language and Vision for Place Recognition

Visual Place Recognition (VPR) often fails under extremeenvironmental changes and perceptual aliasing. Beyond these limita-tions, standard systems cannot perform ’blind’ localization from verbaldescriptions alone, a capability critical for applications such as emer-gency response. To address these challenges, we introduce LaVPR, alarge-scale benchmark that extends existing VPR datasets with over650,000 rich natural-language descriptions. Using LaVPR, we investigatetwo paradigms: Multi-Modal Fusion for enhanced robustness and Cross-Modal Retrieval for language-based localization. Our results show thatlanguage descriptions yield consistent gains in visually degraded condi-tions, with the most significant impact on smaller backbones. Notably,adding language allows compact models to rival the performance of muchlarger vision-only architectures. For cross-modal retrieval, we establish abaseline using Low-Rank Adaptation (LoRA) and Multi-Similarity loss,which substantially outperforms standard contrastive methods acrossvision-language models. Ultimately, LaVPR enables a new class of lo-calization systems that are both resilient to real-world stochasticity andpractical for resource-constrained deployment. Our dataset and code areavailable at https://github.com/oferidan1/LaVPR.