QUALIFIER: Question-Guided Self-Attentive Multimodal Fusion Network for Audio Visual Scene-Aware Dialog

Muchao Ye, Quanzeng You, Fenglong Ma
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引用次数: 3

Abstract

Audio video scene-aware dialog (AVSD) is a new but more challenging visual question answering (VQA) task because of the higher complexity of feature extraction and fusion brought by the additional modalities. Although recent methods have achieved early success in improving feature extraction technique for AVSD, the technique of feature fusion still needs further investigation. In this paper, inspired by the success of self-attention mechanism and the importance of understanding questions for VQA answering, we propose a question-guided self-attentive multi-modal fusion network (QUALIFIER) to improve the AVSD practice in the stage of feature fusion and answer generation. Specifically, after extracting features and learning a comprehensive feature for each modality, we first use the designed self-attentive multi-modal fusion (SMF) module to aggregate each feature with the correlated information learned from others. Later, by prioritizing the question feature, we concatenate it with each fused feature to guide the generation of a natural language response to the question. As for experimental results, QUALIFIER shows better performance than other baseline methods in the large-scale AVSD dataset named DSTC7. Additionally, the human evaluation and ablation study results also demonstrate the effectiveness of our network architecture.
限定语:问题引导的自关注多模态融合网络,用于视听场景感知对话
音频视频场景感知对话(AVSD)是一种新的视觉问答(VQA)任务,由于附加模态带来了更高的特征提取和融合复杂性。虽然近年来的方法在改进AVSD的特征提取技术方面取得了初步的成功,但特征融合技术仍有待进一步研究。在本文中,受自注意机制的成功和理解问题对VQA回答的重要性的启发,我们提出了一个问题引导的自注意多模态融合网络(QUALIFIER),以改进AVSD在特征融合和答案生成阶段的实践。具体而言,在提取特征并学习每个模态的综合特征后,我们首先使用设计的自关注多模态融合(SMF)模块将每个特征与从其他模态学习到的相关信息进行聚合。然后,通过对问题特征进行优先级排序,我们将其与每个融合的特征连接起来,以指导对问题的自然语言响应的生成。实验结果表明,在大规模AVSD数据集DSTC7上,QUALIFIER的性能优于其他基线方法。此外,人类评估和消融研究结果也证明了我们的网络架构的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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