CrossVideo: Self-supervised Cross-modal Contrastive Learning for Point
  Cloud Video Understanding
Yunze Liu Changxi Chen Zifan Wang Li Yi
This paper introduces a novel approach named CrossVideo, which aims to
enhance self-supervised cross-modal contrastive learning in the field of point
cloud video understanding. Traditional supervised learning methods encounter
limitations due to data scarcity and challenges in label acquisition. To
address these issues, we propose a self-supervised learning method that
leverages the cross-modal relationship between point cloud videos and image
videos to acquire meaningful feature representations. Intra-modal and
cross-modal contrastive learning techniques are employed to facilitate
effective comprehension of point cloud video. We also propose a multi-level
contrastive approach for both modalities. Through extensive experiments, we
demonstrate that our method significantly surpasses previous state-of-the-art
approaches, and we conduct comprehensive ablation studies to validate the
effectiveness of our proposed designs.