Dynamic Spatio-Temporal Graph Reasoning for VideoQA With Self-Supervised Event Recognition
Jie Nie, Xin Wang, Runze Hou, Guohao Li, Hong Chen, Wenwu Zhu
Abstract
Video question answering (VideoQA) requires the ability of comprehensively understanding visual contents in videos. Existing VideoQA models mainly focus on scenarios involving a single event with simple object interactions and leave event-centric scenarios involving multiple events with dynamically complex object interactions largely unexplored. These conventional VideoQA models are usually based on features extracted from the global visual signals, making it difficult to capture the object-level and event-level semantics. Although there exists a recent work utilizing a static spatio-temporal graph to explicitly model object interactions in videos, it ignores the dynamic impact of questions for graph construction and fails to exploit the implicit event-level semantic clues in questions. To overcome these limitations, we propose a Self-supervised Dynamic Graph Reasoning (SDGraphR) model for video question answering (VideoQA). Our SDGraphR model learns a question-guided spatio-temporal graph that dynamically encodes intra-frame spatial correlations and inter-frame correspondences between objects in the videos. Furthermore, the proposed SDGraphR model discovers event-level cues from questions to conduct self-supervised learning with an auxiliary event recognition task, which in turn helps to improve its VideoQA performances without using any extra annotations. We carry out extensive experiments to validate the substantial improvements of our proposed SDGraphR model over existing baselines.