This work proposes UGE, a two-stage training strategy that progressively and stably aligns images, text, and spatial structures by combining instruction-guided contrastive learning with graph-based spatial encoding, and introduces \dataset, a spatially grounded dataset that anchors street-view images to structured spat...
Jie Zhang, Xing-Tong Yu, Yuan Fang et al.· Proceedings of the 32nd ACM...· 0 citations
Learning transferable multimodal embeddings for urban environments is challenging because urban understanding is inherently spatial, yet existing datasets and benchmarks lack explicit alignment between street-view images and urban structure. We introduce \dataset, a spatially grounded dataset that anchors street-view i...
Jie Zhang, Xingtong Yu, Yuan Fang et al.· Proceedings of the 32nd ACM...· 0 citations
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