DialogueVPR: Towards Conversational Visual Place Recognition

Researchers introduced DialogueVPR, a conversational visual place recognition system that uses interactive dialogue to improve geo-localization from natural language descriptions. The system, built on the DlgPR framework and the DlgQuest-Cities benchmark, outperforms static retrieval methods by reasoning through ambiguity with a trained questioner model.

arXiv:2607.14115v1 Announce Type: new Abstract: Inspired by how humans communicate spatial information, language-guided geo-localization has gained significant traction for its intuitive and practical value. Despite this progress, most methods still rely on a static, one-shot retrieval paradigm, which fails to handle the ambiguity and incompleteness inherent in real-world natural language descriptions. We propose a paradigm shift to reasoning retrieval and introduce Dialogue Place Recognition DlgPR , which casts localization as an interactive, dialogue-driven reasoning process. To support this new task, we present DlgQuest-Cities, the first large-scale dialogue-based benchmark for place recognition, and a unified reasoning framework that couples a cross-modal multi-level retriever with an intelligent questioner, DQ-pilot. DQ-pilot is trained in a curriculum: supervised fine-tuning on a curated DQ-cities-20k subset followed by reinforcement refinement on a harder DQ-cities-10k split via GRPO. Two task-aligned metrics guide learning: a Discriminative Difficulty Index DDI for curriculum sampling and a Positional Retrieval Gain PRG reward that directly measures retrieval improvement induced by a question. Experiments show this reasoning-based approach significantly outperforms baselines. The code and model are available at https://github.com/Graysonggg/DlgPR.