Semantic Embedding Guided Attention with Explicit Visual Feature Fusion for Video Captioning

Video captioning, which bridges vision and language, is a fundamental yet challenging task in computer vision. To generate accurate and comprehensive sentences, both visual and semantic information is quite important. However, most existing methods simply concatenate different types of features and ignore the interactions between them. In addition, there is a large semantic gap between visual feature space and semantic embedding space, making the task very challenging. To address these issues, we propose a framework named semantic embedding guided attention with Explicit visual Feature Fusion for vidEo CapTioning, EFFECT for short, in which we design an explicit visual-feature fusion (EVF) scheme to capture the pairwise interactions between multiple visual modalities and fuse multimodal visual features of videos in an explicit way. Furthermore, we propose a novel attention mechanism called semantic embedding guided attention (SEGA), which cooperates with the temporal attention to generate a joint attention map. Specifically, in SEGA, the semantic word embedding information is leveraged to guide the model to pay more attention to the most correlated visual features at each decoding stage. In this way, the semantic gap between visual and semantic space is alleviated to some extent. To evaluate the proposed model, we conduct extensive experiments on two widely used datasets, i.e., MSVD and MSR-VTT. The experimental results demonstrate that our approach achieves state-of-the-art results in terms of four evaluation metrics.