V3C - a Research Video Collection
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Abstract (arXiv)
With the widespread use of smartphones as recording devices and the massive growth in bandwidth, the number and volume of video collections has increased significantly in the last years. This poses novel challenges to the management of these large-scale video data and especially to the analysis of and retrieval from such video collections. At the same time, existing video datasets used for research and experimentation are either not large enough to represent current collections or do not reflect the properties of video commonly found on the Internet in terms of content, length, or resolution. In this paper, we introduce the Vimeo Creative Commons Collection, in short V3C, a collection of 28'450 videos (with overall length of about 3'800 hours) published under creative commons license on Vimeo. V3C comes with a shot segmentation for each video, together with the resulting keyframes in original as well as reduced resolution and additional metadata. It is intended to be used from 2019 at the International large-scale TREC Video Retrieval Evaluation campaign (TRECVid).
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Oct 2018.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 4; 2024: 1; 2025: 4; 2026: 1.
12 papers.
- 2021TRECVID 2020: A comprehensive campaign for evaluating video retrieval tasks across multiple application domainscited by 16
- 2023StableVQA: A Deep No-Reference Quality Assessment Model for Video Stabilitycited by 15
- 2024OmniActions: Predicting Digital Actions in Response to Real-World Multimodal Sensory Inputs with LLMscited by 10
- 2022Marine Video Kit: A New Marine Video Dataset for Content-based Analysis and Retrievalcited by 9
- 2023An overview on the evaluated video retrieval tasks at TRECVID 2022cited by 7
- 2023MultiVENT: Multilingual Videos of Events with Aligned Natural Textcited by 7
- 2023Interactive Video Corpus Moment Retrieval using Reinforcement Learningcited by 4
- 2025SKALD: Learning-Based Shot Assembly for Coherent Multi-Shot Video Creationcited by 3
- 2025On the Brittleness of CLIP Text Encoderscited by 3
- 2025Experimental Evaluation of Static Image Sub-Region-Based Search Models Using CLIPcited by 0
- 2025Fusionista2.0: Efficiency Retrieval System for Large-Scale Datasetscited by 0
- 2026Prompting-MammAlps: Fine-Grained Text-to-Video Retrieval for Camera-Trap Datacited by 0
- 2026Prompting-MammAlps: Fine-Grained Text-to-Video Retrieval for Camera-Trap Data10 Jul 2026
- 2025Fusionista2.0: Efficiency Retrieval System for Large-Scale Datasets15 Nov 2025
- 2025On the Brittleness of CLIP Text Encoders6 Nov 2025
- 2025Experimental Evaluation of Static Image Sub-Region-Based Search Models Using CLIP7 Jun 2025
- 2025SKALD: Learning-Based Shot Assembly for Coherent Multi-Shot Video Creation11 Mar 2025
- 2024OmniActions: Predicting Digital Actions in Response to Real-World Multimodal Sensory Inputs with LLMs6 May 2024
- 2023StableVQA: A Deep No-Reference Quality Assessment Model for Video Stability9 Aug 2023
- 2023MultiVENT: Multilingual Videos of Events with Aligned Natural Text6 Jul 2023
- 2023An overview on the evaluated video retrieval tasks at TRECVID 202222 Jun 2023
- 2023Interactive Video Corpus Moment Retrieval using Reinforcement Learning19 Feb 2023
- 2022Marine Video Kit: A New Marine Video Dataset for Content-based Analysis and Retrieval23 Sep 2022
- 2021TRECVID 2020: A comprehensive campaign for evaluating video retrieval tasks across multiple application domains27 Apr 2021