Advancements in Visual Language Models for Remote Sensing: Datasets, Capabilities, and Enhancement Techniques

• URL: http://arxiv.org/abs/2410.17283v2
• Date: Thu, 14 Nov 2024 11:42:52 GMT
• Title: Advancements in Visual Language Models for Remote Sensing: Datasets, Capabilities, and Enhancement Techniques
• Authors: Lijie Tao, Haokui Zhang, Haizhao Jing, Yu Liu, Kelu Yao, Chao Li, Xizhe Xue,
• Abstract summary: We review the fundamental theories related to visual language models (VLMs) and the datasets constructed for them in remote sensing. We categorize the improvement methods into three main parts according to the core components ofVLMs and provide a detailed introduction and comparison of these methods.
• Score: 9.248637518957445
• License:
• Abstract: Recently, the remarkable success of ChatGPT has sparked a renewed wave of interest in artificial intelligence (AI), and the advancements in visual language models (VLMs) have pushed this enthusiasm to new heights. Differring from previous AI approaches that generally formulated different tasks as discriminative models, VLMs frame tasks as generative models and align language with visual information, enabling the handling of more challenging problems. The remote sensing (RS) field, a highly practical domain, has also embraced this new trend and introduced several VLM-based RS methods that have demonstrated promising performance and enormous potential. In this paper, we first review the fundamental theories related to VLM, then summarize the datasets constructed for VLMs in remote sensing and the various tasks they addressed. Finally, we categorize the improvement methods into three main parts according to the core components of VLMs and provide a detailed introduction and comparison of these methods. A project associated with this review has been created at https://github.com/taolijie11111/VLMs-in-RS-review.

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