MF-RSVLM Enhancing Remote Sensing with Multi-Feature Fusion
📝 Original Paper Info
- Title: FUSE-RSVLM Feature Fusion Vision-Language Model for Remote Sensing- ArXiv ID: 2512.24022
- Date: 2025-12-30
- Authors: Yunkai Dang, Donghao Wang, Jiacheng Yang, Yifan Jiang, Meiyi Zhu, Yuekun Yang, Cong Wang, Qi Fan, Wenbin Li, Yang Gao
📝 Abstract
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.💡 Summary & Analysis
1. **New Model Design**: This research introduces a new design principle that overcomes the limitations of existing models. Imagine traditional models as fixed structures, but our approach allows for more flexible adjustments. 2. **Efficient Learning Algorithm**: A novel algorithm makes the learning process more efficient. Think of it like improving an athlete's training regimen to boost performance. 3. **Validation Across Various Datasets**: The proposed methodology shows excellent performance across a wide range of datasets. This is akin to having equipment that works well in various environments.📄 Full Paper Content (ArXiv Source)
📊 논문 시각자료 (Figures)
