The following is a set of baseline methods from different methods. These results were obtained without using the training set. Only the first frame of the each testing sequence was used to estimate key parameters if needed. Please refer to [1] for details. Please refer to the contest page for related codes.
[1] Z. Li, F. Xiong, J. Zhou, J. Lu, Z. Zhao and Y. Qian, "Material-Guided Multiview Fusion Network for Hyperspectral Object Tracking," IEEE Transactions on Geoscience and Remote Sensing, vol. code
[2] Zhu J, Lai S, Chen X, et al. Visual prompt multi-modal tracking[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2023: 9516-9526. code
[3] Y. Chen, Q. Yuan, Y. Tang, Y. Xiao, J. He and L. Zhang, "SPIRIT: Spectral Awareness Interaction Network With Dynamic Template for Hyperspectral Object Tracking," in IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-16, 2024. code
[4] Z. Li, F. Xiong, J. Zhou, J. Lu and Y. Qian, "Learning a Deep Ensemble Network With Band Importance for Hyperspectral Object Tracking," in IEEE Transactions on Image Processing, vol. 32, pp. 2901-2914, 2023, doi: 10.1109/TIP.2023.3263109. code
[5] R. Muszyński and H. Luong, "Helios: Hyperspectral Hindsight Ostracker," 2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Athens, Greece, 2023, pp. 1-5, doi: 10.1109/WHISPERS61460.2023.10430711. code
[6] Li Z, Xiong F, Lu J, et al. Multi-domain universal representation learning for hyperspectral object tracking[J]. Pattern Recognition, 2025, 162: 111389. code