Keyword-aware Multi-modal Enhancement Attention for Video Question Answering

Duo Chen, Fuwei Zhang, Shirou Ou, Ruomei Wang
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Abstract

Video question answering (VideoQA) is an intriguing topic in the field of visual language. Most of the current VideoQA models directly harness the global video information to answer questions. However, in VideoQA task, the answers associated with the questions merely appear in a few video contents, and other contents are invalid and redundant information. Therefore, VideoQA is vulnerable to be interfered by a large number of irrelevant contents. To address this challenge, we propose a Keyword-aware Multi-modal Enhancement Attention model for VideoQA. Specifically, a multi-factor keyword extraction (MFKE) algorithm is proposed to emphasize the crucial information in multimodal feature extraction. Furthermore, based on attention mechanisms, a keyword-aware enhancement attention (KAEA) module is designed to correlate the information associated with multiple modalities and fuse multimodal features. The experimental results on publicly available large VideoQA datasets, namely TVQA+ and LifeQA, demonstrate the effectiveness of our model.
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基于关键字感知的多模态视频问答增强注意事项
视频问答(VideoQA)是视觉语言领域中一个有趣的话题。目前的大多数VideoQA模型直接利用全局视频信息来回答问题。然而,在VideoQA任务中,与问题相关的答案只出现在少数视频内容中,其他内容都是无效的冗余信息。因此,VideoQA很容易受到大量无关内容的干扰。为了解决这一挑战,我们提出了一个用于VideoQA的关键字感知多模态增强注意模型。针对多模态特征提取中的关键信息,提出了一种多因素关键字提取算法。在注意机制的基础上,设计了关键词感知增强注意(KAEA)模块,实现多模态信息的关联,融合多模态特征。在公开的大型VideoQA数据集(TVQA+和LifeQA)上的实验结果证明了该模型的有效性。
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