LuoJiaHOG: A Hierarchy Oriented Geo-aware Image Caption Dataset for Remote Sensing Image-Text Retrival

• URL: http://arxiv.org/abs/2403.10887v1
• Date: Sat, 16 Mar 2024 10:46:14 GMT
• Title: LuoJiaHOG: A Hierarchy Oriented Geo-aware Image Caption Dataset for Remote Sensing Image-Text Retrival
• Authors: Yuanxin Zhao, Mi Zhang, Bingnan Yang, Zhan Zhang, Jiaju Kang, Jianya Gong,
• Abstract summary: We introduce an image caption dataset LuojiaHOG, which is geospatial-aware, label-extension-friendly and comprehensive-captioned. LuojiaHOG involves the hierarchical spatial sampling, classification system to Open Geospatial Consortium (OGC) standards, and detailed caption generation. We also propose a CLIP-based Image Semantic Enhancement Network (CISEN) to promote sophisticated ITR.
• Score: 8.656768875730904
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image-text retrieval (ITR) plays a significant role in making informed decisions for various remote sensing (RS) applications. Nonetheless, creating ITR datasets containing vision and language modalities not only requires significant geo-spatial sampling area but also varing categories and detailed descriptions. To this end, we introduce an image caption dataset LuojiaHOG, which is geospatial-aware, label-extension-friendly and comprehensive-captioned. LuojiaHOG involves the hierarchical spatial sampling, extensible classification system to Open Geospatial Consortium (OGC) standards, and detailed caption generation. In addition, we propose a CLIP-based Image Semantic Enhancement Network (CISEN) to promote sophisticated ITR. CISEN consists of two components, namely dual-path knowledge transfer and progressive cross-modal feature fusion. Comprehensive statistics on LuojiaHOG reveal the richness in sampling diversity, labels quantity and descriptions granularity. The evaluation on LuojiaHOG is conducted across various state-of-the-art ITR models, including ALBEF, ALIGN, CLIP, FILIP, Wukong, GeoRSCLIP and CISEN. We use second- and third-level labels to evaluate these vision-language models through adapter-tuning and CISEN demonstrates superior performance. For instance, it achieves the highest scores with WMAP@5 of 88.47\% and 87.28\% on third-level ITR tasks, respectively. In particular, CISEN exhibits an improvement of approximately 1.3\% and 0.9\% in terms of WMAP@5 compared to its baseline. These findings highlight CISEN advancements accurately retrieving pertinent information across image and text. LuojiaHOG and CISEN can serve as a foundational resource for future RS image-text alignment research, facilitating a wide range of vision-language applications.

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