End-to-End Dense Video Captioning with Masked Transformer
Open accessAbstract
Dense video captioning aims to generate text descriptions for all events in an untrimmed video. This involves both detecting and describing events. Therefore, all previous methods on dense video captioning tackle this problem by building two models, i.e. an event proposal and a captioning model, for these two sub-problems. The models are either trained separately or in alternation. This prevents direct influence of the language description to the event proposal, which is important for generating accurate descriptions. To address this problem, we propose an end-to-end transformer model for dense video captioning. The encoder encodes the video into appropriate representations. The proposal decoder decodes from the encoding with different anchors to form video event proposals. The captioning decoder employs a masking network to restrict its attention to the proposal event over the encoding feature. This masking network converts the event proposal to a differentiable mask, which ensures the consistency between the proposal and captioning during training. In addition, our model employs a self-attention mechanism, which enables the use of efficient non-recurrent structure during encoding and leads to performance improvements. We demonstrate the effectiveness of this end-to-end model on ActivityNet Captions and YouCookII datasets, where we achieved 10.12 and 6.58 METEOR score, respectively.
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Kaleido-BERT is a VLP Transformer model tailor-made for the fashion domain.
“Kaleido-BERT [212] is a VLP Transformer model tailor-made for the fashion domain.”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [212]
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Background
- Authors
- Zhou, L., Zhou, Y., Corso, J. J., Socher, R., & Xiong, C.
- Affiliations
- University of Michigan (US)
- Salesforce (United States) (US)
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 8739-8748
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963351113
- Cited by
- 605
- References
- 45
- Citations by year
- 2026: 27 · 2025: 58 · 2024: 80 · 2023: 111 · 2022: 107 · 2021: 114 · 2020: 64 · 2019: 38 · 2018: 6
- FWCI
- 16.47
- 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)
- Funders
- Defense Advanced Research Projects Agency (FA8750, FA8750-17-2-0112)
- Army Research Office (W911NF-15-1-0354, W911NF-15-1-, W911NF, W911NF-15)
- 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 › Video Analysis and Summarization
- Keywords
- dense video captioning, masked transformer, end-to-end learning, attention masking, self-attention, video description generation, untrimmed video, ActivityNet Captions
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