The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Video Captioning With Attention-Based LSTM and Semantic Consistency
Abstract
Recent progress in using long short-term memory (LSTM) for image captioning has motivated the exploration of their applications for video captioning. By taking a video as a sequence of features, an LSTM model is trained on video-sentence pairs and learns to associate a video to a sentence. However, most existing methods compress an entire video shot or frame into a static representation, without considering attention mechanism which allows for selecting salient features. Furthermore, existing approaches usually model the translating error, but ignore the correlations between sentence semantics and visual content. To tackle these issues, we propose a novel end-to-end framework named aLSTMs, an attention-based LSTM model with semantic consistency, to transfer videos to natural sentences. This framework integrates attention mechanism with LSTM to capture salient structures of video, and explores the correlation between multimodal representations (i.e., words and visual content) for generating sentences with rich semantic content. Specifically, we first propose an attention mechanism that uses the dynamic weighted sum of local two-dimensional convolutional neural network representations. Then, an LSTM decoder takes these visual features at time t and the word-embedding feature at time t-1 to generate important words. Finally, we use multimodal embedding to map the visual and sentence features into a joint space to guarantee the semantic consistence of the sentence description and the video visual content. Experiments on the benchmark datasets demonstrate that our method using single feature can achieve competitive or even better results than the state-of-the-art baselines for video captioning in both BLEU and METEOR.
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Cites
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MirrorGAN: Learning Text-To-Image Generation by Redescription
Qiao, T., Zhang, J., Xu, D., & Tao, D. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00160 · Cited by 644
“dresses visual similarities. Attention models have been extensively exploited in computer vision and natural language processing, for instance in object detection [21,6,18,40], image/video captioning [33,9,31], visual question answering [2,32,35, 22], and neural machine translation [19,8]. Attention can be modeled spa…”
BackgroundDeep Multimodal Representation Learning: A Survey
Guo, W., Wang, J., & Wang, S. (2019) · IEEE Access · doi:10.1109/access.2019.2916887 · Cited by 535
Task-Adaptive Attention for Image Captioning
Yan, C., Hao, Y., Li, L., Yin, J., Liu, A., Mao, Z., Chen, Z., & Gao, X. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3067449 · Cited by 367
Enhancing transportation systems via deep learning: A survey
Wang, Y., Zhang, D., Liu, Y., Dai, B., & Lee, L. H. (2019) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2018.12.004 · Cited by 312
Attention, please! A survey of neural attention models in deep learning
de Santana Correia, A., & Colombini, E. L. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10148-x · Cited by 298
Metadata
- Authors
- Gao, L., Guo, Z., Zhang, H., Xu, X., & Shen, H. T.
- Venue
- IEEE Transactions on Multimedia
- Published
- 2017-09
- Volume
- 19
- Issue
- 9
- Pages
- 2045-2055
- ISSN
- 1520-9210 (ISSN-L)
- 1941-0077
- OpenAlex
- W2739107216
- Cited by
- 637
- References
- 10
- FWCI
- 31.79
- Type
- journal article
- Language
- en
- Open access
- closed
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