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.

翻译：变化检测是计算机视觉中的一项基础任务，它通过处理双时相图像对来区分语义上发生改变和未改变的区域。大型语言模型（LLMs）因其卓越的特征提取能力已在多个领域得到应用，并在众多下游任务中展现出潜力。在本研究中，我们利用预训练LLM的强大能力，从大规模数据集中提取特征图，并采用一个辅助网络进行变化检测。与现有仅专注于获取高质量特征图的基于LLM的变化检测方法不同，我们的方法强调对这些特征图进行操纵以增强其语义相关性。