Hyperspectral imagery contains abundant spectral information beyond the visible RGB bands, providing rich discriminative details about objects in a scene. Leveraging such data has the potential to enhance visual tracking performance. While prior hyperspectral trackers employ CNN or hybrid CNN-Transformer architectures, we propose a novel approach HPFormer on Transformers to capitalize on their powerful representation learning capabilities. The core of HPFormer is a Hyperspectral Hybrid Attention (HHA) module which unifies feature extraction and fusion within one component through token interactions. Additionally, a Transform Band Module (TBM) is introduced to selectively aggregate spatial details and spectral signatures from the full hyperspectral input for injecting informative target representations. Extensive experiments demonstrate state-of-the-art performance of HPFormer on benchmark NIR and VIS tracking datasets. Our work provides new insights into harnessing the strengths of transformers and hyperspectral fusion to advance robust object tracking.

HPFormer是一种基于Transformer的新方法，利用其强大的表示学习能力来提高视觉跟踪性能，并通过Hyperspectral Hybrid Attention（HHA）模块实现特征提取和融合，以及通过Transform Band Module (TBM)从完整的高光谱输入中选择性地聚合空间细节和光谱特征来注入有信息的目标表示，通过在基准NIR和VIS跟踪数据集上进行的广泛实验表明，HPFormer具有最先进的性能，从而提供了利用Transformer和高光谱融合来改进鲁棒目标跟踪的新见解。

HPFormer：高光谱图像提示目标跟踪