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Heterogeneous Memory Enhanced Multimodal Attention Model for Video Question Answering
Open accessAbstract
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.
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“Currently, the most common existing methods use attention [125, 127, 129] and memory [126] mechanisms to efficiently learn visual artifacts and the semantic correlations that allow questions to be answered accurately.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [126]
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1 of the 38 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
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Metadata
- Authors
- Fan, C., Zhang, X., Zhang, S., Wang, W., Zhang, C., & Huang, H.
- Affiliations
- Jingdong (China) (CN)
- University of Pittsburgh (US)
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 1999-2007
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2954199749
- Cited by
- 293
- References
- 38
- Citations by year
- 2026: 16 · 2025: 23 · 2024: 49 · 2023: 65 · 2022: 46 · 2021: 59 · 2020: 31 · 2019: 4
- FWCI
- 11.42
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- video question answering, heterogeneous memory, multimodal attention, multimodal fusion, global context modeling, appearance and motion features, multi-step reasoning
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