Scale, Don't Fine-tune: Guiding Multimodal LLMs for Efficient Visual Place Recognition at Test-Time

• URL: http://arxiv.org/abs/2509.02129v1
• Date: Tue, 02 Sep 2025 09:25:13 GMT
• Title: Scale, Don't Fine-tune: Guiding Multimodal LLMs for Efficient Visual Place Recognition at Test-Time
• Authors: Jintao Cheng, Weibin Li, Jiehao Luo, Xiaoyu Tang, Zhijian He, Jin Wu, Yao Zou, Wei Zhang,
• Abstract summary: Current approaches, including Vision Foundation Models (VFMs) and Multimodal Large Language Models (MLLMs), enhance semantic understanding but suffer from high computational overhead and limited cross-domain transferability when fine-tuned.<n>We propose a novel framework employing Test-Time Scaling (TTS) that leverages vision-language alignment capabilities through Guidance-based methods for direct similarity scoring.<n>Our approach eliminates two-stage processing by employing structured prompts that generate length-controllable scoring outputs.
• Score: 12.659582318581606
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual Place Recognition (VPR) has evolved from handcrafted descriptors to deep learning approaches, yet significant challenges remain. Current approaches, including Vision Foundation Models (VFMs) and Multimodal Large Language Models (MLLMs), enhance semantic understanding but suffer from high computational overhead and limited cross-domain transferability when fine-tuned. To address these limitations, we propose a novel zero-shot framework employing Test-Time Scaling (TTS) that leverages MLLMs' vision-language alignment capabilities through Guidance-based methods for direct similarity scoring. Our approach eliminates two-stage processing by employing structured prompts that generate length-controllable JSON outputs. The TTS framework with Uncertainty-Aware Self-Consistency (UASC) enables real-time adaptation without additional training costs, achieving superior generalization across diverse environments. Experimental results demonstrate significant improvements in cross-domain VPR performance with up to 210$\times$ computational efficiency gains.

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