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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.15943 |
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| _version_ | 1866909222860488704 |
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| author | Li, Zhenglin Huang, Yangchen Zhu, Mengran Zhang, Jingyu Chang, JingHao Liu, Houze |
| author_facet | Li, Zhenglin Huang, Yangchen Zhu, Mengran Zhang, Jingyu Chang, JingHao Liu, Houze |
| contents | Change detection is a fundamental task in computer vision that processes a bi-temporal image pair to differentiate between semantically altered and unaltered regions. Large language models (LLMs) have been utilized in various domains for their exceptional feature extraction capabilities and have shown promise in numerous downstream applications. In this study, we harness the power of a pre-trained LLM, extracting feature maps from extensive datasets, and employ an auxiliary network to detect changes. Unlike existing LLM-based change detection methods that solely focus on deriving high-quality feature maps, our approach emphasizes the manipulation of these feature maps to enhance semantic relevance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_15943 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Advanced Feature Manipulation for Enhanced Change Detection Leveraging Natural Language Models Li, Zhenglin Huang, Yangchen Zhu, Mengran Zhang, Jingyu Chang, JingHao Liu, Houze Computer Vision and Pattern Recognition Change detection is a fundamental task in computer vision that processes a bi-temporal image pair to differentiate between semantically altered and unaltered regions. Large language models (LLMs) have been utilized in various domains for their exceptional feature extraction capabilities and have shown promise in numerous downstream applications. In this study, we harness the power of a pre-trained LLM, extracting feature maps from extensive datasets, and employ an auxiliary network to detect changes. Unlike existing LLM-based change detection methods that solely focus on deriving high-quality feature maps, our approach emphasizes the manipulation of these feature maps to enhance semantic relevance. |
| title | Advanced Feature Manipulation for Enhanced Change Detection Leveraging Natural Language Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.15943 |