Syntax-Aware Action Targeting for Video Captioning
By Qi Zheng, Chaoyue Wang, Dacheng Tao. 2020
155 citations recorded by the source corpus.
Existing methods on video captioning have made great efforts to identify objects/instances in videos, but few of them emphasize the prediction of action. As a result, the learned models are likely to depend heavily on the prior of training data, such as the co-occurrence of objects, which may cause an enormous divergence between the generated descriptions and the video content. In this paper, we explicitly emphasize the importance of \textit{action} by predicting visually-related syntax components including \textit{subject}, \textit{object} and \textit{predicate}. Specifically, we propose a Syntax-Aware Action Targeting (SAAT) module that firstly builds a self-attended scene representation to draw global dependence among multiple objects within a scene, and then decodes the visually-related syntax components by setting different queries. After targeting the \textit{action}, indicated by \textit{predicate}, our captioner learns an attention distribution over the \textit{predicate} and the previously predicted words to guide the generation of the next word. Comprehensive experiments on MSVD and MSR-VTT datasets demonstrate the efficacy of the proposed model.
Subfields: Computer Vision and Pattern Recognition, Natural Language Generation
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Citations in the corpus
- GIT: A Generative Image-to-text Transformer for Vision and Language (2022, background): “[130] Qi Zheng, Chaoyue Wang, and Dacheng Tao.”