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Video captioning is a challenging task that requires a deep understanding ofvisual scenes. State-of-the-art methods generate captions using eitherscene-level or object-level information but without explicitly modeling objectinteractions. Thus, they often fail to make visually grounded predictions, andare sensitive to spurious correlations. In this paper, we propose a novelspatio-temporal graph model for video captioning that exploits objectinteractions in space and time. Our model builds interpretable links and isable to provide explicit visual grounding. To avoid unstable performance causedby the variable number of objects, we further propose an object-aware knowledgedistillation mechanism, in which local object information is used to regularizeglobal scene features. We demonstrate the efficacy of our approach throughextensive experiments on two benchmarks, showing our approach yieldscompetitive performance with interpretable predictions.

Spatio-Temporal Graph for Video Captioning with Knowledge Distillation