eMoE-Tracker: Environmental MoE-based Transformer for Robust Event-guided Object Tracking
<|reference_start|>eMoE-Tracker: Environmental MoE-based Transformer for Robust Event-guided Object Tracking: The unique complementarity of frame-based and event cameras for high frame rate object tracking has recently inspired some research attempts to develop multi-modal fusion approaches. However, these methods directly fuse both modalities and thus ignore the environmental attributes, e.g., motion blur, illumination variance, occlusion, scale variation, etc. Meanwhile, no interaction between search and template features makes distinguishing target objects and backgrounds difficult. As a result, performance degradation is induced especially in challenging conditions. This paper proposes a novel and effective Transformer-based event-guided tracking framework, called eMoE-Tracker, which achieves new SOTA performance under various conditions. Our key idea is to disentangle the environment into several learnable attributes to dynamically learn the attribute-specific features for better interaction and discriminability between the target information and background. To achieve the goal, we first propose an environmental Mix-of-Experts (eMoE) module that is built upon the environmental Attributes Disentanglement to learn attribute-specific features and environmental Attributes Gating to assemble the attribute-specific features by the learnable attribute scores dynamically. The eMoE module is a subtle router that fine-tunes the transformer backbone more efficiently. We then introduce a contrastive relation modeling (CRM) module to improve interaction and discriminability between the target information and background. Extensive experiments on diverse event-based benchmark datasets showcase the superior performance of our eMoE-Tracker compared to the prior arts.<|reference_end|>
@article{chen2024emoe-tracker:,
    title={eMoE-Tracker: Environmental MoE-based Transformer for Robust
  Event-guided Object Tracking},
    author={Yucheng Chen and Lin Wang},
    journal={arXiv preprint arXiv:2406.20024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.20024},
    primaryClass={cs.CV}
}