Cross-Modal Reasoning with Event Correlation for Video Question Answering

• URL: http://arxiv.org/abs/2312.12721v1
• Date: Wed, 20 Dec 2023 02:30:39 GMT
• Title: Cross-Modal Reasoning with Event Correlation for Video Question Answering
• Authors: Chengxiang Yin, Zhengping Che, Kun Wu, Zhiyuan Xu, Qinru Qiu, Jian Tang
• Abstract summary: We introduce the dense caption modality as a new auxiliary and distill event-correlated information from it to infer the correct answer. We employ cross-modal reasoning modules for explicitly modeling inter-modal relationships and aggregating relevant information across different modalities. We propose a question-guided self-adaptive multi-modal fusion module to collect the question-oriented and event-correlated evidence through multi-step reasoning.
• Score: 32.332251488360185
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video Question Answering (VideoQA) is a very attractive and challenging research direction aiming to understand complex semantics of heterogeneous data from two domains, i.e., the spatio-temporal video content and the word sequence in question. Although various attention mechanisms have been utilized to manage contextualized representations by modeling intra- and inter-modal relationships of the two modalities, one limitation of the predominant VideoQA methods is the lack of reasoning with event correlation, that is, sensing and analyzing relationships among abundant and informative events contained in the video. In this paper, we introduce the dense caption modality as a new auxiliary and distill event-correlated information from it to infer the correct answer. To this end, we propose a novel end-to-end trainable model, Event-Correlated Graph Neural Networks (EC-GNNs), to perform cross-modal reasoning over information from the three modalities (i.e., caption, video, and question). Besides the exploitation of a brand new modality, we employ cross-modal reasoning modules for explicitly modeling inter-modal relationships and aggregating relevant information across different modalities, and we propose a question-guided self-adaptive multi-modal fusion module to collect the question-oriented and event-correlated evidence through multi-step reasoning. We evaluate our model on two widely-used benchmark datasets and conduct an ablation study to justify the effectiveness of each proposed component.

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