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Video Question Answering (VideoQA), aiming to correctly answer the givenquestion based on understanding multi-modal video content, is challenging dueto the rich video content. From the perspective of video understanding, a goodVideoQA framework needs to understand the video content at different semanticlevels and flexibly integrate the diverse video content to distillquestion-related content. To this end, we propose a LightweightVisual-Linguistic Reasoning framework named LiVLR. Specifically, LiVLR firstutilizes the graph-based Visual and Linguistic Encoders to obtain multi-grainedvisual and linguistic representations. Subsequently, the obtainedrepresentations are integrated with the devised Diversity-awareVisual-Linguistic Reasoning module (DaVL). The DaVL considers the differencebetween the different types of representations and can flexibly adjust theimportance of different types of representations when generating thequestion-related joint representation, which is an effective and generalrepresentation integration method. The proposed LiVLR is lightweight and showsits performance advantage on two VideoQA benchmarks, MRSVTT-QA and KnowIT VQA.Extensive ablation studies demonstrate the effectiveness of LiVLR keycomponents.

LiVLR: A Lightweight Visual-Linguistic Reasoning Framework for Video Question Answering