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Attention-Based Multimodal Fusion for Video Description
Open accessAbstract
Current methods for video description are based on encoder-decoder sentence generation using recurrent neural networks (RNNs). Recent work has demonstrated the advantages of integrating temporal attention mechanisms into these models, in which the decoder network predicts each word in the description by selectively giving more weight to encoded features from specific time frames. Such methods typically use two different types of features: image features (from an object classification model), and motion features (from an action recognition model), combined by naive concatenation in the model input. Because different feature modalities may carry task-relevant information at different times, fusing them by naive concatenation may limit the model's ability to dynamically determine the relevance of each type of feature to different parts of the description. In this paper, we incorporate audio features in addition to the image and motion features. To fuse these three modalities, we introduce a multimodal attention model that can selectively utilize features from different modalities for each word in the output description. Combining our new multimodal attention model with standard temporal attention outperforms state-of-the-art methods on two standard datasets: YouTube2Text and MSR-VTT.
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“Since its introduction, the AM has gained wide adoption in the computer vision community due to its spectral capabilities for many multimodal applications such as video description [233, 234], salient object detection [235], etc.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [233]
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Metadata
- Authors
- Hori, C., Hori, T., Lee, T.-Y., Zhang, Z., Harsham, B., Hershey, J. R., Marks, T. K., & Sumi, K.
- Affiliations
- Mitsubishi Electric (United States) (US)
- Aoyama Gakuin University (JP)
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 4203-4212
- OpenAlex
- W2584992898
- Cited by
- 401
- References
- 38
- Citations by year
- 2026: 11 · 2025: 24 · 2024: 41 · 2023: 47 · 2022: 54 · 2021: 55 · 2020: 63 · 2019: 71 · 2018: 30 · 2017: 4
- FWCI
- 13.22
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (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 › Video Analysis and Summarization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Keywords
- audio description, multimodal fusion, multimodal attention, temporal attention, audio features, imaging features, motion features, video captioning, MSR-VTT
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