LiCAF: LiDAR-Camera Asymmetric Fusion for Gait Recognition
<|reference_start|>LiCAF: LiDAR-Camera Asymmetric Fusion for Gait Recognition: Gait recognition is a biometric technology that identifies individuals by using walking patterns. Due to the significant achievements of multimodal fusion in gait recognition, we consider employing LiDAR-camera fusion to obtain robust gait representations. However, existing methods often overlook intrinsic characteristics of modalities, and lack fine-grained fusion and temporal modeling. In this paper, we introduce a novel modality-sensitive network LiCAF for LiDAR-camera fusion, which employs an asymmetric modeling strategy. Specifically, we propose Asymmetric Cross-modal Channel Attention (ACCA) and Interlaced Cross-modal Temporal Modeling (ICTM) for cross-modal valuable channel information selection and powerful temporal modeling. Our method achieves state-of-the-art performance (93.9% in Rank-1 and 98.8% in Rank-5) on the SUSTech1K dataset, demonstrating its effectiveness.<|reference_end|>
@article{deng2024licaf:,
    title={LiCAF: LiDAR-Camera Asymmetric Fusion for Gait Recognition},
    author={Yunze Deng, Haijun Xiong, Bin Feng},
    journal={arXiv preprint arXiv:2406.12355},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12355},
    primaryClass={cs.CV}
}