Heterogeneous Memory Enhanced Multimodal Attention Model for Video Question Answering

Chenyou Fan, Xiaofan Zhang, Shu Zhang, Wensheng Wang, Chi Zhang, Heng Huang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 1999-2007

Abstract


In this paper, we propose a novel end-to-end trainable Video Question Answering (VideoQA) framework with three major components: 1) a new heterogeneous memory which can effectively learn global context information from appearance and motion features; 2) a redesigned question memory which helps understand the complex semantics of question and highlights queried subjects; and 3) a new multimodal fusion layer which performs multi-step reasoning by attending to relevant visual and textual hints with self-updated attention. Our VideoQA model firstly generates the global context-aware visual and textual features respectively by interacting current inputs with memory contents. After that, it makes the attentional fusion of the multimodal visual and textual representations to infer the correct answer. Multiple cycles of reasoning can be made to iteratively refine attention weights of the multimodal data and improve the final representation of the QA pair. Experimental results demonstrate our approach achieves state-of-the-art performance on four VideoQA benchmark datasets.

Related Material


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[bibtex]
@InProceedings{Fan_2019_CVPR,
author = {Fan, Chenyou and Zhang, Xiaofan and Zhang, Shu and Wang, Wensheng and Zhang, Chi and Huang, Heng},
title = {Heterogeneous Memory Enhanced Multimodal Attention Model for Video Question Answering},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}