mlsanityData to 7 Oct 2026

CREATE: A Benchmark for Chinese Short Video Retrieval and Title Generation

arXiv 2203.16763 ↗First posted 31 Mar 2022cs.CVcited by 5

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Abstract (arXiv)

Previous works of video captioning aim to objectively describe the video's actual content, which lacks subjective and attractive expression, limiting its practical application scenarios. Video titling is intended to achieve this goal, but there is a lack of a proper benchmark. In this paper, we propose to CREATE, the first large-scale Chinese shoRt vidEo retrievAl and Title gEneration benchmark, to facilitate research and application in video titling and video retrieval in Chinese. CREATE consists of a high-quality labeled 210K dataset and two large-scale 3M/10M pre-training datasets, covering 51 categories, 50K+ tags, 537K manually annotated titles and captions, and 10M+ short videos. Based on CREATE, we propose a novel model ALWIG which combines video retrieval and video titling tasks to achieve the purpose of multi-modal ALignment WIth Generation with the help of video tags and a GPT pre-trained model. CREATE opens new directions for facilitating future research and applications on video titling and video retrieval in the field of Chinese short videos.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 29 of its 39 references that are in the map. First posted on arXiv 31 Mar 2022.

Builds on

29 of 39 references.

References · 29

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17 more
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Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 2; 2025: 1.

5 papers.

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  5. 2024CBVS: A Large-Scale Chinese Image-Text Benchmark for Real-World Short Video Search Scenarioscited by 0