The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Jointly Modeling Embedding and Translation to Bridge Video and Language
Open accessAbstract
Automatically describing video content with natural language is a fundamental challenge of computer vision. Re-current Neural Networks (RNNs), which models sequence dynamics, has attracted increasing attention on visual interpretation. However, most existing approaches generate a word locally with the given previous words and the visual content, while the relationship between sentence semantics and visual content is not holistically exploited. As a result, the generated sentences may be contextually correct but the semantics (e.g., subjects, verbs or objects) are not true. This paper presents a novel unified framework, named Long Short-Term Memory with visual-semantic Embedding (LSTM-E), which can simultaneously explore the learning of LSTM and visual-semantic embedding. The former aims to locally maximize the probability of generating the next word given previous words and visual content, while the latter is to create a visual-semantic embedding space for enforcing the relationship between the semantics of the entire sentence and visual content. The experiments on YouTube2Text dataset show that our proposed LSTM-E achieves to-date the best published performance in generating natural sentences: 45.3% and 31.0% in terms of BLEU@4 and METEOR, respectively. Superior performances are also reported on two movie description datasets (M-VAD and MPII-MD). In addition, we demonstrate that LSTM-E outperforms several state-of-the-art techniques in predicting Subject-Verb-Object (SVO) triplets.
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Dense-Captioning Events in Videos
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Exploring Visual Relationship for Image Captioning
Yao, T., Pan, Y., Li, Y., & Mei, T. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01264-9_42 · Cited by 870
Self-citationBoosting Image Captioning with Attributes
Yao, T., Pan, Y., Li, Y., Qiu, Z., & Mei, T. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.524 · Cited by 733
Self-citationEnd-to-End Learning of Visual Representations From Uncurated Instructional Videos
Miech, A., Alayrac, J.-B., Smaira, L., Laptev, I., Sivic, J., & Zisserman, A. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00990 · Cited by 580
Metadata
- Authors
- Pan, Y., Mei, T., Yao, T., Li, H., & Rui, Y.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 4594-4602
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1573040851
- Cited by
- 563
- References
- 11
- FWCI
- 31.36
- Type
- conference paper
- Language
- en
- Open access
- green
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