The DOI matches its Crossref record; 37 of the 38 references it cites were found in a registry or an index.
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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Cites
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(2017) · Cited by 11,625
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Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
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Research works printed but not linked: 2
- kembhavi, 2017, Textbook question answering for multimodal machine comprehension, CVPR
- paszke, 2017, On Automatic Differentiation
Cited by
Sorted by citations
Learning Memory-Guided Normality for Anomaly Detection
Park, H., Noh, J., & Ham, B. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01438 · Cited by 1,034
Less is More: CLIPBERT for Video-and-Language Learning via Sparse Sampling
Lei, J., Li, L., Zhou, L., Gan, Z., Berg, T. L., Bansal, M., & Liu, J. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00725 · Cited by 639
The multi-modal fusion in visual question answering: a review of attention mechanisms
Lu, S., Liu, M., Yin, L., Yin, Z., Liu, X., & Zheng, W. (2023) · PeerJ Computer Science · doi:10.7717/peerj-cs.1400 · Cited by 492
Bayoudh, K., Knani, R., Hamdaoui, F., & Mtibaa, A. (2022) · The Visual Computer · doi:10.1007/s00371-021-02166-7 · Cited by 456
Cross-Modal Causal Relational Reasoning for Event-Level Visual Question Answering
Liu, Y., Li, G., & Lin, L. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3284038 · Cited by 373
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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