Spatio-Temporal Graph for Video Captioning with Knowledge Distillation

B. Pan, H. Cai, DA Huang, KH Lee, A. Gaidon, E. Adeli, JC Niebles

Published in CVPR 2020 - June 2020

Links: arxiv, video, poster, bibtex

CVPR 2020 ST graph video captioning teaser


Video captioning is a challenging task that requires a deep understanding of visual scenes. State-of-the-art methods generate captions using either scene-level or object-level information but without explicitly modeling object interactions. Thus, they often fail to make visually grounded predictions, and are sensitive to spurious correlations. In this paper, we propose a novel spatio-temporal graph model for video captioning that exploits object interactions in space and time. Our model builds interpretable links and is able to provide explicit visual grounding. To avoid unstable performance caused by the variable number of objects, we further propose an object-aware knowledge distillation mechanism, in which local object information is used to regularize global scene features. We demonstrate the efficacy of our approach through extensive experiments on two benchmarks, showing our approach yields competitive performance with interpretable predictions.



ST Graph CVPR 2020 poster


    title={Spatio-Temporal Graph for Video Captioning with Knowledge Distillation},
    author={Boxiao Pan and Haoye Cai and De-An Huang and Kuan-Hui Lee and Adrien Gaidon and Ehsan Adeli and Juan Carlos Niebles},