Parallel and Sharing Attention Mechanism for Cross-Media Annotation and Retrieval

There are still many problems in cross-media annotation and retrieval. The semantic gap between the features of multiple media and the low-level visual features do not carry semantics, and the presence of noise has a great impact. This paper proposes a cross-media semantic learning model that combines Parallel and sharing attention mechanism, which sets semantic graph network topology and node information in the graph neural network. Testing on experimental data show that the network structure proposed in this paper can achieve better accuracy than traditional methods.