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Video question answering (VideoQA) is a challenging video understanding task that requires a comprehensive understanding of multimodal information and accurate answers to related questions. Most existing VideoQA models use Graph Neural Networks (GNN) to capture temporal–spatial interactions between objects. Despite achieving certain success, we argue that current schemes have two limitations: (i) existing graph-based methods require stacking multi-layers of GNN to capture high-order relations between objects, which inevitably introduces irrelevant noise; (ii) neglecting the unique self-supervised signals in the high-order relational structures among multiple objects that can facilitate more accurate QA. To this end, we propose a novel Multi-scale Self-supervised Hypergraph Contrastive Learning (MSHCL) framework for VideoQA. Specifically, we first segment the video from multiple temporal dimensions …