FUSE-RSVLM：用于遥感的特征融合视觉-语言模型
FUSE-RSVLM: Feature Fusion Vision-Language Model for Remote Sensing
AI总结：
FUSE-RSVLM通过多特征融合技术提升遥感图像的视觉-语言处理能力，有效解决细粒度特征提取和视觉遗忘问题，在遥感分类、图像描述和VQA任务中表现优异。
摘要：
大型视觉-语言模型（VLMs）在各种任务中表现出色。然而，这些VLMs在应用于遥感领域时面临显著挑战，因为遥感图像与自然图像存在本质差异。现有的遥感VLMs往往无法提取细粒度的视觉特征，并在深度语言处理中出现视觉遗忘问题。为此，我们引入了MF-RSVLM，一种多特征融合的遥感视觉-语言模型，能够有效提取和融合视觉特征以实现遥感理解。MF-RSVLM学习多尺度的视觉表示，并结合全局上下文与局部细节，提高对遥感场景中小而复杂的结构的捕捉能力。一个递归的视觉特征注入方案确保语言模型始终基于视觉证据，并在生成过程中减少视觉遗忘。在多样化的遥感基准测试中，MF-RSVLM在遥感分类、图像描述和VQA任务中均实现了最先进的或高度竞争性的性能。我们的代码已公开在https://github.com/Yunkaidang/RSVLM。
英文摘要：
Large vision-language models (VLMs) exhibit strong performance across various tasks. However, these VLMs encounter significant challenges when applied to the remote sensing domain due to the inherent differences between remote sensing images and natural images. Existing remote sensing VLMs often fail to extract fine-grained visual features and suffer from visual forgetting during deep language processing. To address this, we introduce MF-RSVLM, a Multi-Feature Fusion Remote Sensing Vision--Language Model that effectively extracts and fuses visual features for RS understanding. MF-RSVLM learns multi-scale visual representations and combines global context with local details, improving the capture of small and complex structures in RS scenes. A recurrent visual feature injection scheme ensures the language model remains grounded in visual evidence and reduces visual forgetting during generation. Extensive experiments on diverse RS benchmarks show that MF-RSVLM achieves state-of-the-art or highly competitive performance across remote sensing classification, image captioning, and VQA tasks. Our code is publicly available at https://github.com/Yunkaidang/RSVLM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
