Aquila-plus: Prompt-Driven Visual-Language Models for Pixel-Level Remote Sensing Image Understanding
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arXiv
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916476170010624 |
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| author | Lu, Kaixuan |
| author_facet | Lu, Kaixuan |
| contents | The recent development of vision language models (VLMs) has led to significant advances in visual-language integration through visual instruction tuning, and they have rapidly evolved in the field of remote sensing image understanding, demonstrating their powerful capabilities. However, existing RSVLMs mainly focus on image-level or frame-level understanding, making it difficult to achieve fine-grained pixel-level visual-language alignment. Additionally, the lack of mask-based instructional data limits their further development. In this paper, we propose a mask-text instruction tuning method called Aquila-plus, which extends the capabilities of RSVLMs to achieve pixel-level visual understanding by incorporating fine-grained mask regions into language instructions. To achieve this, we first meticulously constructed a mask region-text dataset containing 100K samples, and then designed a visual-language model by injecting pixel-level representations into a large language model (LLM). Specifically, Aquila-plus uses a convolutional CLIP as the visual encoder and employs a mask-aware visual extractor to extract precise visual mask features from high-resolution inputs. Experimental results demonstrate that Aquila-plus outperforms existing methods in various region understanding tasks, showcasing its novel capabilities in pixel-level instruction tuning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_06142 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Aquila-plus: Prompt-Driven Visual-Language Models for Pixel-Level Remote Sensing Image Understanding Lu, Kaixuan Computer Vision and Pattern Recognition Artificial Intelligence The recent development of vision language models (VLMs) has led to significant advances in visual-language integration through visual instruction tuning, and they have rapidly evolved in the field of remote sensing image understanding, demonstrating their powerful capabilities. However, existing RSVLMs mainly focus on image-level or frame-level understanding, making it difficult to achieve fine-grained pixel-level visual-language alignment. Additionally, the lack of mask-based instructional data limits their further development. In this paper, we propose a mask-text instruction tuning method called Aquila-plus, which extends the capabilities of RSVLMs to achieve pixel-level visual understanding by incorporating fine-grained mask regions into language instructions. To achieve this, we first meticulously constructed a mask region-text dataset containing 100K samples, and then designed a visual-language model by injecting pixel-level representations into a large language model (LLM). Specifically, Aquila-plus uses a convolutional CLIP as the visual encoder and employs a mask-aware visual extractor to extract precise visual mask features from high-resolution inputs. Experimental results demonstrate that Aquila-plus outperforms existing methods in various region understanding tasks, showcasing its novel capabilities in pixel-level instruction tuning. |
| title | Aquila-plus: Prompt-Driven Visual-Language Models for Pixel-Level Remote Sensing Image Understanding |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2411.06142 |