Cross-Attentional Spatio-Temporal Semantic Graph Networks for Video Question Answering.

Area of Science:

• Artificial Intelligence
• Computer Vision
• Natural Language Processing

Background:

• Video Question Answering (VideoQA) is challenging due to complex spatio-temporal content and multimodal relations.
• Existing methods use attention mechanisms but struggle to integrate inter- and intra-modality correlations uniformly.
• Effective integration of multimodal information is crucial for improving VideoQA comprehension.

Purpose of the Study:

• To propose a novel VideoQA model, Cross-Attentional Spatio-Temporal Semantic Graph Networks (CASSG).
• To address the limitation of current methods in uniformly integrating inter- and intra-modality correlations.
• To enhance the comprehension ability of VideoQA systems by exploring fine-grained interactions.

Main Methods:

• A multi-head multi-hop attention module explores cross-modal interactions.
• Heterogeneous graphs (multi-stream spatio-temporal semantic graphs) are constructed for synchronous reasoning of correlations.
• A global and local information fusion method is employed to infer the final answer.

Main Results:

• The proposed CASSG model demonstrates effectiveness on three public VideoQA datasets.
• Experimental results show superior performance compared to state-of-the-art methods.
• The model successfully integrates diverse correlations for improved VideoQA performance.

Conclusions:

• The CASSG model offers a novel approach to VideoQA by effectively integrating multimodal information.
• The proposed attention and graph-based methods enable synchronous reasoning of inter- and intra-modality correlations.
• CASSG represents a significant advancement in VideoQA research, achieving state-of-the-art results.