Guided Graph Attention Learning for Video-Text Matching

As a bridge between videos and natural languages, video-text matching has been a hot multimedia research topic in recent years. Such cross-modal retrieval is usually achieved by learning a common embedding space where videos and text captions are directly comparable. It is still challenging because existing visual representations cannot exploit semantic correlations within videos well, resulting in a mismatch with semantic concepts that are contained in the corresponding text descriptions. In this article, we propose a new Guided Graph Attention Learning (GGAL) model to enhance video embedding learning by capturing important region-level semantic concepts within the spatiotemporal space. Our model builds connections between object regions and performs hierarchical graph reasoning on both frame-level and whole video–level region graphs. During this process, global context is used to guide attention learning on this hierarchical graph topology so that the learned overall video embedding can focus on essential semantic concepts and can be better aligned with text captions. Experiments on commonly used benchmarks validate that GGAL outperforms many recent video-text retrieval methods with a clear margin. As multimedia data in dynamic environments becomes critically important, we also validate GGAL learned video-text representations that can be generalized well to unseen out-of-domain data via cross-dataset evaluations. To further investigate the interpretability of our model, we visualize attention weights learned by GGAL models. We find that GGAL successfully focuses on key semantic concepts in the video and has complementary attention on the context parts based on different ways of building region graphs.