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

• URL: http://arxiv.org/abs/2111.14547v2
• Date: Tue, 30 Nov 2021 02:18:26 GMT
• Title: LiVLR: A Lightweight Visual-Linguistic Reasoning Framework for Video Question Answering
• Authors: Jingjing Jiang, Ziyi Liu, and Nanning Zheng
• Abstract summary: We propose a Lightweight Visual-Linguistic Reasoning framework named LiVLR. LiVLR first utilizes the graph-based Visual and Linguistic ablations to obtain multi-grained visual and linguistic representations. The proposed LiVLR is lightweight and shows its performance advantage on two VideoQA benchmarks.
• Score: 50.11756459499762
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video Question Answering (VideoQA), aiming to correctly answer the given question based on understanding multi-modal video content, is challenging due to the rich video content. From the perspective of video understanding, a good VideoQA framework needs to understand the video content at different semantic levels and flexibly integrate the diverse video content to distill question-related content. To this end, we propose a Lightweight Visual-Linguistic Reasoning framework named LiVLR. Specifically, LiVLR first utilizes the graph-based Visual and Linguistic Encoders to obtain multi-grained visual and linguistic representations. Subsequently, the obtained representations are integrated with the devised Diversity-aware Visual-Linguistic Reasoning module (DaVL). The DaVL considers the difference between the different types of representations and can flexibly adjust the importance of different types of representations when generating the question-related joint representation, which is an effective and general representation integration method. The proposed LiVLR is lightweight and shows its performance advantage on two VideoQA benchmarks, MRSVTT-QA and KnowIT VQA. Extensive ablation studies demonstrate the effectiveness of LiVLR key components.

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