Semantic-Enhanced Underwater Videos Multi-Label Classification Network Based on Structural Graph Convolution

Underwater visual degradation makes it difficult for the image modality to represent video semantics, while label sparsity in underwater scenes leads to weak inter-category correlations, thereby degrading the performance of multi-label classification. To address these issues, this paper proposes a Semantic-Enhanced Underwater Videos Multi-Label Classification Network Based on Structural Graph Convolution (SEMGCN). Specifically, the proposed method first disentangles the image and text modalities into shared and private representations, and enhances feature representation capability through orthogonal constraints and feature reconstruction. Moreover, a Cross-Modal Category-Aware Module (CCAM) is constructed to model interactions between image and text features and perform bidirectional cross-attention with category-label text embeddings, thereby generating category-aware initial node representations. Furthermore, to alleviate the limitation of semantic propagation caused by sparse label co-occurrence, a Structural Graph Convolutional Network (SGCN) is proposed. By integrating explicit co-occurrence relationships with implicit structural similarity relationships, the proposed model collaboratively captures both explicit and latent semantic associations, thereby improving multi-label classification performance under label-sparse conditions. Experiments were conducted on the self-constructed Underwater Video Multi-label Classification Dataset (UVMC) and the public MLSV2018 dataset. The experimental results show that SEMGCN achieves Average Precision scores of 0.8645 and 0.8388 on UVMC and MLSV2018, respectively, demonstrating the effectiveness of the proposed method.