DialogueVPR: Towards Conversational Visual Place Recognition
Researchers have introduced DialogueVPR, a novel paradigm that casts visual place recognition as an interactive, dialogue-driven reasoning process. By coupling a cross-modal retriever with an intelligent questioner called DQ-pilot, this framework effectively resolves ambiguous real-world descriptions and significantly outperforms traditional static retrieval methods.
Computer Science > Artificial Intelligence
Title:DialogueVPR: Towards Conversational Visual Place Recognition
View PDF HTML (experimental)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.
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Source: arXiv cs.AI Recent
