DialogueVPR: Towards Conversational Visual Place Recognition
Inspired by human communication of spatial information, language-guided geo-localization has gained traction but relies on static one-shot retrieval, failing to handle ambiguity. This paper proposes a paradigm shift to reasoning retrieval with Dialogue Place Recognition (DlgPR), which casts localization as an interactive dialogue-driven process. The authors introduce DlgQuest-Cities, the first large-scale dialogue-based benchmark for place recognition, and a unified framework with a cross-modal retriever and intelligent questioner DQ-pilot. DQ-pilot is trained via curriculum learning: supervised fine-tuning on DQ-cities-20k then reinforcement refinement on DQ-cities-10k using GRPO. Two metrics guide learning: Discriminative Difficulty Index (DDI) and Positional Retrieval Gain (PRG). Experiments show significant improvements over baselines.
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[Submitted on 8 May 2026]
Title:DialogueVPR: Towards Conversational Visual Place Recognition
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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 this https URL.
Comments: Accepted to CVPR 2026
Subjects:
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.14115 [cs.AI]
(or arXiv:2607.14115v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.14115
arXiv-issued DOI via DataCite
Submission history
From: Yukun Song [view email] [v1] Fri, 8 May 2026 12:04:43 UTC (1,657 KB)
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