AFter: Attention-based Fusion Router for RGBT Tracking
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909189462294528 |
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| author | Lu, Andong Wang, Wanyu Li, Chenglong Tang, Jin Luo, Bin |
| author_facet | Lu, Andong Wang, Wanyu Li, Chenglong Tang, Jin Luo, Bin |
| contents | Multi-modal feature fusion as a core investigative component of RGBT tracking emerges numerous fusion studies in recent years. However, existing RGBT tracking methods widely adopt fixed fusion structures to integrate multi-modal feature, which are hard to handle various challenges in dynamic scenarios. To address this problem, this work presents a novel \emph{A}ttention-based \emph{F}usion rou\emph{ter} called AFter, which optimizes the fusion structure to adapt to the dynamic challenging scenarios, for robust RGBT tracking. In particular, we design a fusion structure space based on the hierarchical attention network, each attention-based fusion unit corresponding to a fusion operation and a combination of these attention units corresponding to a fusion structure. Through optimizing the combination of attention-based fusion units, we can dynamically select the fusion structure to adapt to various challenging scenarios. Unlike complex search of different structures in neural architecture search algorithms, we develop a dynamic routing algorithm, which equips each attention-based fusion unit with a router, to predict the combination weights for efficient optimization of the fusion structure. Extensive experiments on five mainstream RGBT tracking datasets demonstrate the superior performance of the proposed AFter against state-of-the-art RGBT trackers. We release the code in https://github.com/Alexadlu/AFter. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_02717 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AFter: Attention-based Fusion Router for RGBT Tracking Lu, Andong Wang, Wanyu Li, Chenglong Tang, Jin Luo, Bin Computer Vision and Pattern Recognition Multi-modal feature fusion as a core investigative component of RGBT tracking emerges numerous fusion studies in recent years. However, existing RGBT tracking methods widely adopt fixed fusion structures to integrate multi-modal feature, which are hard to handle various challenges in dynamic scenarios. To address this problem, this work presents a novel \emph{A}ttention-based \emph{F}usion rou\emph{ter} called AFter, which optimizes the fusion structure to adapt to the dynamic challenging scenarios, for robust RGBT tracking. In particular, we design a fusion structure space based on the hierarchical attention network, each attention-based fusion unit corresponding to a fusion operation and a combination of these attention units corresponding to a fusion structure. Through optimizing the combination of attention-based fusion units, we can dynamically select the fusion structure to adapt to various challenging scenarios. Unlike complex search of different structures in neural architecture search algorithms, we develop a dynamic routing algorithm, which equips each attention-based fusion unit with a router, to predict the combination weights for efficient optimization of the fusion structure. Extensive experiments on five mainstream RGBT tracking datasets demonstrate the superior performance of the proposed AFter against state-of-the-art RGBT trackers. We release the code in https://github.com/Alexadlu/AFter. |
| title | AFter: Attention-based Fusion Router for RGBT Tracking |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.02717 |