The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Video Question Answering via Gradually Refined Attention over Appearance and Motion
Abstract
Recently image question answering (ImageQA) has gained lots of attention in the research community. However, as its natural extension, video question answering (VideoQA) is less explored. Although both tasks look similar, VideoQA is more challenging mainly because of the complexity and diversity of videos. As such, simply extending the ImageQA methods to videos is insufficient and suboptimal. Particularly, working with the video needs to model its inherent temporal structure and analyze the diverse information it contains. In this paper, we consider exploiting the appearance and motion information resided in the video with a novel attention mechanism. More specifically, we propose an end-to-end model which gradually refines its attention over the appearance and motion features of the video using the question as guidance. The question is processed word by word until the model generates the final optimized attention. The weighted representation of the video, as well as other contextual information, are used to generate the answer. Extensive experiments show the advantages of our model compared to other baseline models. We also demonstrate the effectiveness of our model by analyzing the refined attention weights during the question answering procedure.
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Cites
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TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
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Two-Stream Convolutional Networks for Action Recognition in Videos
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Neural Collaborative Filtering
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Self-citationLarge-Scale Video Classification with Convolutional Neural Networks
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Cited by
Flamingo: a visual language model for few-shot learning
Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-1723 · Cited by 966
“[130] Dejing Xu, Zhou Zhao, Jun Xiao, Fei Wu, Hanwang Zhang, Xiangnan He, and Yueting Zhuang.”
BackgroundInfluentialMVBench: A Comprehensive Multi-modal Video Understanding Benchmark
Li, K., Wang, Y., He, Y., Li, Y., Wang, Y., Liu, Y., Wang, Z., Xu, J., Chen, G., Lou, P., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.02095 · Cited by 300
Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis
Fu, C., Dai, Y., Luo, Y., Li, L., Ren, S., Zhang, R., Wang, Z., Zhou, C., Shen, Y., Zhang, M., et al. (2025) · 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52734.2025.02245 · Cited by 291
Heterogeneous Memory Enhanced Multimodal Attention Model for Video Question Answering
Fan, C., Zhang, X., Zhang, S., Wang, W., Zhang, C., & Huang, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00210 · Cited by 293
ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering
Yu, Z., Xu, D., Yu, J., Yu, T., Zhao, Z., Zhuang, Y., & Tao, D. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33019127 · Cited by 281
“2017) MSVD Automatic OE 1,970 50,505 10s MSRVTT-QA (Xu et al. 2017) MSRVTT Automatic OE 10,000 243,680 15s TGIF-QA (Jang et al.”
BackgroundMethodInfluentialSelf-citation
Metadata
- Authors
- Xu, D., Zhao, Z., Xiao, J., Wu, F., Zhang, H., He, X., & Zhuang, Y.
- Venue
- Proceedings of the 25th ACM international conference on Multimedia
- Publisher
- ACM
- Published
- 2017-10-23
- Pages
- 1645-1653
- OpenAlex
- W2765716052
- Cited by
- 429
- References
- 45
- FWCI
- 5.02
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Xu, D., Zhao, Z., Xiao, J., Wu, F., Zhang, H., He, X., & Zhuang, Y. (2017). Video Question Answering via Gradually Refined Attention over Appearance and Motion. Proceedings of the 25th ACM international conference on Multimedia. https://doi.org/10.1145/3123266.3123427
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