Fine-Grained Video-Text Retrieval With Hierarchical Graph Reasoning

Shizhe Chen, Yida Zhao, Qin Jin, Qi Wu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 10638-10647


Cross-modal retrieval between videos and texts has attracted growing attentions due to the rapid emergence of videos on the web. The current dominant approach is to learn a joint embedding space to measure cross-modal similarities. However, simple embeddings are insufficient to represent complicated visual and textual details, such as scenes, objects, actions and their compositions. To improve fine-grained video-text retrieval, we propose a Hierarchical Graph Reasoning (HGR) model, which decomposes video-text matching into global-to-local levels. The model disentangles text into a hierarchical semantic graph including three levels of events, actions, entities, and generates hierarchical textual embeddings via attention-based graph reasoning. Different levels of texts can guide the learning of diverse and hierarchical video representations for cross-modal matching to capture both global and local details. Experimental results on three video-text datasets demonstrate the advantages of our model. Such hierarchical decomposition also enables better generalization across datasets and improves the ability to distinguish fine-grained semantic differences. Code will be released at

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[pdf] [supp] [arXiv]
author = {Chen, Shizhe and Zhao, Yida and Jin, Qin and Wu, Qi},
title = {Fine-Grained Video-Text Retrieval With Hierarchical Graph Reasoning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}