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

arXiv: https://arxiv.org/abs/2203.16763 · HTML: https://mlsanity.com/paper/2203.16763/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 39 references that are in the map.
- First posted on arXiv 31 Mar 2022.

## Builds on (most cited first, 29 of 29)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://mlsanity.com/paper/2102.03334/) (2021)
- [Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks](https://mlsanity.com/paper/2004.06165/) (2020)
- [Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval](https://mlsanity.com/paper/2104.00650/) (2021)
- [HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips](https://mlsanity.com/paper/1906.03327/) (2019)
- [Multimodal Few-Shot Learning with Frozen Language Models](https://mlsanity.com/paper/2106.13884/) (2021)
- [CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval](https://mlsanity.com/paper/2104.08860/) (2021)
- [Towards Automatic Learning of Procedures from Web Instructional Videos](https://mlsanity.com/paper/1703.09788/) (2017)
- [End-to-End Learning of Visual Representations from Uncurated Instructional Videos](https://mlsanity.com/paper/1912.06430/) (2019)
- [HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training](https://mlsanity.com/paper/2005.00200/) (2020)
- [ActBERT: Learning Global-Local Video-Text Representations](https://mlsanity.com/paper/2011.07231/) (2020)
- [UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation](https://mlsanity.com/paper/2002.06353/) (2020)
- [TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrieval](https://mlsanity.com/paper/2001.09099/) (2020)
- [TGIF: A New Dataset and Benchmark on Animated GIF Description](https://mlsanity.com/paper/1604.02748/) (2016)
- [Object Relational Graph with Teacher-Recommended Learning for Video Captioning](https://mlsanity.com/paper/2002.11566/) (2020)
- [VALUE: A Multi-Task Benchmark for Video-and-Language Understanding Evaluation](https://mlsanity.com/paper/2106.04632/) (2021)
- [MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning](https://mlsanity.com/paper/2005.05402/) (2020)
- [Spatio-Temporal Dynamics and Semantic Attribute Enriched Visual Encoding for Video Captioning](https://mlsanity.com/paper/1902.10322/) (2019)
- [Transform and Tell: Entity-Aware News Image Captioning](https://mlsanity.com/paper/2004.08070/) (2020)
- [Open-book Video Captioning with Retrieve-Copy-Generate Network](https://mlsanity.com/paper/2103.05284/) (2021)
- [Object-aware Aggregation with Bidirectional Temporal Graph for Video Captioning](https://mlsanity.com/paper/1906.04375/) (2019)
- [Learning Spatiotemporal Features via Video and Text Pair Discrimination](https://mlsanity.com/paper/2001.05691/) (2020)
- [Understanding Chinese Video and Language via Contrastive Multimodal Pre-Training](https://mlsanity.com/paper/2104.09411/) (2021)
- [Title Generation for User Generated Videos](https://mlsanity.com/paper/1608.07068/) (2016)
- [Comprehensive Information Integration Modeling Framework for Video Titling](https://mlsanity.com/paper/2006.13608/) (2020)
- [Poet: Product-oriented Video Captioner for E-commerce](https://mlsanity.com/paper/2008.06880/) (2020)

## Built on by (most cited first, 5 of 5)

- [Youku-mPLUG: A 10 Million Large-scale Chinese Video-Language Dataset for Pre-training and Benchmarks](https://mlsanity.com/paper/2306.04362/) (2023)
- [Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey](https://mlsanity.com/paper/2312.03014/) (2023)
- [Data Collection-free Masked Video Modeling](https://mlsanity.com/paper/2409.06665/) (2024)
- [On Controlled Change: Generative AI's Impact on Professional Authority in Journalism](https://mlsanity.com/paper/2510.19792/) (2025)
- [CBVS: A Large-Scale Chinese Image-Text Benchmark for Real-World Short Video Search Scenarios](https://mlsanity.com/paper/2401.10475/) (2024)

## 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.