Multimodal multilevel attention for semi-supervised skeleton-based gesture recognition

Although skeleton-based gesture recognition using supervised learning has achieved promising results, the reliance on extensive annotated data poses significant costs. This paper addresses the challenge of semi-supervised skeleton-based gesture recognition, to effectively learn feature representations from labeled and unlabeled data. To resolve this problem, we propose a novel multimodal multilevel attention network designed for semi-supervised learning. This model utilizes the self-attention mechanism to polymerize multimodal and multilevel complementary semantic information of the hand skeleton, designing a multimodal multilevel contrastive loss to measure feature similarity. Specifically, our method explores the relationships between joint, bone, and motion to learn more discriminative feature representations. Considering the hierarchy of the hand skeleton, the skeleton data is divided into multilevel to capture complementary semantic information. Furthermore, the multimodal contrastive loss measures similarity among these multilevel representations. The proposed method demonstrates improved performance in semi-supervised skeleton-based gesture recognition tasks, as evidenced by experiments on the SHREC-17 and DHG 14/28 datasets.