no code implementations • 8 May 2024 • Pengcheng Shao, Tianyang Xu, Zhangyong Tang, Linze Li, Xiao-Jun Wu, Josef Kittler
There is currently strong interest in improving visual object tracking by augmenting the RGB modality with the output of a visual event camera that is particularly informative about the scene motion.
no code implementations • 30 Apr 2024 • Zhangyong Tang, Tianyang Xu, ZhenHua Feng, XueFeng Zhu, He Wang, Pengcheng Shao, Chunyang Cheng, Xiao-Jun Wu, Muhammad Awais, Sara Atito, Josef Kittler
We propose a new method based on a mixture of experts, namely MoETrack, as a baseline fusion strategy.
1 code implementation • 21 Dec 2023 • Chunyang Cheng, Tianyang Xu, Xiao-Jun Wu, Hui Li, Xi Li, Zhangyong Tang, Josef Kittler
Advanced image fusion methods are devoted to generating the fusion results by aggregating the complementary information conveyed by the source images.
1 code implementation • 4 Sep 2023 • Zhangyong Tang, Tianyang Xu, XueFeng Zhu, Xiao-Jun Wu, Josef Kittler
In this context, we seek to uncover the potential of harnessing generative techniques to address the critical challenge, information fusion, in multi-modal tracking.
Ranked #7 on Rgb-T Tracking on LasHeR
1 code implementation • 21 Aug 2022 • Xue-Feng Zhu, Tianyang Xu, Zhangyong Tang, Zucheng Wu, Haodong Liu, Xiao Yang, Xiao-Jun Wu, Josef Kittler
To demonstrate the benefits of training on a larger RGB-D data set in general, and RGBD1K in particular, we develop a transformer-based RGB-D tracker, named SPT, as a baseline for future visual object tracking studies using the new dataset.
no code implementations • 23 Jan 2022 • Zhangyong Tang, Tianyang Xu, Xiao-Jun Wu
This survey can be treated as a look-up-table for researchers who are concerned about RGBT tracking.
1 code implementation • 22 Jan 2022 • Zhangyong Tang, Tianyang Xu, Xiao-Jun Wu
Specifically, different from traditional Siamese trackers, which only obtain one search image during the process of picking up template-search image pairs, an extra search sample adjacent to the original one is selected to predict the temporal transformation, resulting in improved robustness of tracking performance. As for multi-modal tracking, constrained to the limited RGBT datasets, the adaptive fusion sub-network is appended to our method at the decision level to reflect the complementary characteristics contained in two modalities.
1 code implementation • 21 Jan 2022 • Zhangyong Tang, Tianyang Xu, Hui Li, Xiao-Jun Wu, XueFeng Zhu, Josef Kittler
The effectiveness of the proposed decision-level fusion strategy owes to a number of innovative contributions, including a dynamic weighting of the RGB and TIR contributions and a linear template update operation.