Video In Sentences Out
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Abstract (arXiv)
We present a system that produces sentential descriptions of video: who did what to whom, and where and how they did it. Action class is rendered as a verb, participant objects as noun phrases, properties of those objects as adjectival modifiers in those noun phrases, spatial relations between those participants as prepositional phrases, and characteristics of the event as prepositional-phrase adjuncts and adverbial modifiers. Extracting the information needed to render these linguistic entities requires an approach to event recognition that recovers object tracks, the trackto-role assignments, and changing body posture.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Aug 2014.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 1; 2024: 2.
7 papers.
- 2021Perspectives and Prospects on Transformer Architecture for Cross-Modal Tasks with Language and Visioncited by 14
- 2021Trajectory Prediction with Linguistic Representationscited by 13
- 2024A Survey on Integrated Sensing, Communication, and Computationcited by 12
- 2021A Comprehensive Review of the Video-to-Text Problemcited by 5
- 2022Dual-Level Decoupled Transformer for Video Captioningcited by 2
- 2023CLearViD: Curriculum Learning for Video Descriptioncited by 1
- 2024Multi-Modal interpretable automatic video captioningcited by 0
- 2024Multi-Modal interpretable automatic video captioning11 Nov 2024
- 2024A Survey on Integrated Sensing, Communication, and Computation15 Aug 2024
- 2023CLearViD: Curriculum Learning for Video Description8 Nov 2023
- 2022Dual-Level Decoupled Transformer for Video Captioning6 May 2022
- 2021Trajectory Prediction with Linguistic Representations19 Oct 2021
- 2021A Comprehensive Review of the Video-to-Text Problem27 Mar 2021
- 2021Perspectives and Prospects on Transformer Architecture for Cross-Modal Tasks with Language and Vision6 Mar 2021