Joint Temporal Pooling for Improving Skeleton-based Action Recognition
<|reference_start|>Joint Temporal Pooling for Improving Skeleton-based Action Recognition: In skeleton-based human action recognition, temporal pooling is a critical step for capturing spatiotemporal relationship of joint dynamics. Conventional pooling methods overlook the preservation of motion information and treat each frame equally. However, in an action sequence, only a few segments of frames carry discriminative information related to the action. This paper presents a novel Joint Motion Adaptive Temporal Pooling (JMAP) method for improving skeleton-based action recognition. Two variants of JMAP, frame-wise pooling and joint-wise pooling, are introduced. The efficacy of JMAP has been validated through experiments on the popular NTU RGB+D 120 and PKU-MMD datasets.<|reference_end|>
@article{gunasekara2024joint,
    title={Joint Temporal Pooling for Improving Skeleton-based Action Recognition},
    author={Shanaka Ramesh Gunasekara, Wanqing Li, Jack Yang, Philip Ogunbona},
    journal={2023 International Conference on Digital Image Computing:
  Techniques and Applications, DICTA 2023},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09356},
    primaryClass={cs.CV}
}