用少样本数据实现可见光与热成像融合检测，提升复杂环境下的识别能力。
FSMODNet: A Closer Look at Few-Shot Detection in Multispectral Data
- 通过可变形注意力融合可见光与热成像特征
- 在低数据量下仍保持优异检测性能
- 适合跨模态目标检测研究者参考
少样本多光谱目标检测（FSMOD）旨在用极少标注数据实现可见光与热成像模态下的物体检测。本文深入探索该任务，提出名为FSMODNet的框架，利用跨模态特征融合提升检测性能。通过可变形注意力机制有效结合可见光与热成像的独特优势，该方法在复杂光照与环境条件下表现出强适应性。在两个公开数据集上的实验表明，其在低数据场景下优于多个基于先进模型构建的基线。所有代码、模型及实验数据划分详见https://anonymous.4open.science/r/Test-B48D。
原文摘要 · Abstract (English)
Few-shot multispectral object detection (FSMOD) addresses the challenge of detecting objects across visible and thermal modalities with minimal annotated data. In this paper, we explore this complex task and introduce a framework named "FSMODNet" that leverages cross-modality feature integration to improve detection performance even with limited labels. By effectively combining the unique strengths of visible and thermal imagery using deformable attention, the proposed method demonstrates robust adaptability in complex illumination and environmental conditions. Experimental results on two public datasets show effective object detection performance in challenging low-data regimes, outperforming several baselines we established from state-of-the-art models. All code, models, and experimental data splits can be found at https://anonymous.4open.science/r/Test-B48D.
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