AddressVLM: Cross-view Alignment Tuning for Image Address Localization using Large Vision-Language Models
Keywords: Image Address Localization; Large Vision Language Model; Cross-view Alignment; Supervised Fine-tuning
Abstract: Large visual language models (LVLMs) have demonstrated impressive performance in coarse-grained geo-localization at the country or city level, but they struggle with fine-grained street-level localization within urban areas. In this paper, we explore integrating city-wide address localization capabilities into LVLMs, facilitating flexible address-related question answering using street-view images. A key challenge is that the street-view visual question-and-answer (VQA) data provides only microscopic visual cues, leading to subpar performance in fine-tuned models. To tackle this issue, we incorporate perspective-invariant satellite images as macro cues and propose cross-view alignment tuning including a satellite-view and street-view image grafting mechanism, along with an automatic alignment label generation mechanism. This helps build connections between street-view images through cross-view matching, thus enhancing LVLM's global understanding of street distribution. We name our proposed model AddressVLM consisting of two-stage training protocols: cross-view alignment tuning and address localization tuning. Furthermore, we have constructed two street-view VQA datasets based on image address localization datasets from Pittsburgh and San Francisco. Qualitative and quantitative evaluations demonstrate that AddressVLM outperforms counterpart LVLMs by over 9% and 12% in average address localization accuracy on the Pitts-VQA and SF-Base-VQA datasets, respectively.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 2061
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