# V3C - a Research Video Collection

arXiv: https://arxiv.org/abs/1810.04401 · HTML: https://mlsanity.com/paper/1810.04401/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2018.

## Built on by (most cited first, 12 of 12)

- [TRECVID 2020: A comprehensive campaign for evaluating video retrieval tasks across multiple application domains](https://mlsanity.com/paper/2104.13473/) (2021)
- [StableVQA: A Deep No-Reference Quality Assessment Model for Video Stability](https://mlsanity.com/paper/2308.04904/) (2023)
- [OmniActions: Predicting Digital Actions in Response to Real-World Multimodal Sensory Inputs with LLMs](https://mlsanity.com/paper/2405.03901/) (2024)
- [Marine Video Kit: A New Marine Video Dataset for Content-based Analysis and Retrieval](https://mlsanity.com/paper/2209.11518/) (2022)
- [An overview on the evaluated video retrieval tasks at TRECVID 2022](https://mlsanity.com/paper/2306.13118/) (2023)
- [MultiVENT: Multilingual Videos of Events with Aligned Natural Text](https://mlsanity.com/paper/2307.03153/) (2023)
- [Interactive Video Corpus Moment Retrieval using Reinforcement Learning](https://mlsanity.com/paper/2302.09522/) (2023)
- [SKALD: Learning-Based Shot Assembly for Coherent Multi-Shot Video Creation](https://mlsanity.com/paper/2503.08010/) (2025)
- [On the Brittleness of CLIP Text Encoders](https://mlsanity.com/paper/2511.04247/) (2025)
- [Prompting-MammAlps: Fine-Grained Text-to-Video Retrieval for Camera-Trap Data](https://mlsanity.com/paper/2607.09876/) (2026)
- [Experimental Evaluation of Static Image Sub-Region-Based Search Models Using CLIP](https://mlsanity.com/paper/2506.06938/) (2025)
- [Fusionista2.0: Efficiency Retrieval System for Large-Scale Datasets](https://mlsanity.com/paper/2511.12255/) (2025)

## 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).