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Insights on the V3C2 Dataset

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arxiv 2105.01475 v1 pith:VKMNG474 submitted 2021-05-04 cs.MM

Luca Rossetto , Klaus Schoeffmann , Abraham Bernstein This is my paper · ORCID
classification cs.MM
keywords datasetvideodatasetsinsightsresearchshardsv3c2areas
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For research results to be comparable, it is important to have common datasets for experimentation and evaluation. The size of such datasets, however, can be an obstacle to their use. The Vimeo Creative Commons Collection (V3C) is a video dataset designed to be representative of video content found on the web, containing roughly 3800 hours of video in total, split into three shards. In this paper, we present insights on the second of these shards (V3C2) and discuss their implications for research areas, such as video retrieval, for which the dataset might be particularly useful. We also provide all the extracted data in order to simplify the use of the dataset.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Robust Relevance Feedback for Interactive Known-Item Video Search

    cs.IR 2025-05 reject novelty 6.0 of 10

    A pairwise relative-feedback scheme with a predictive user model that filters misaligned embedding sub-perceptions raises known-item video search rank-1 rates above PicHunter, though the evaluation uses a synthetic user.

  2. diveXplore at the Video Browser Showdown 2024

    cs.MM 2025-08 conditional novelty 4.0 of 10

    diveXplore 2024 integrates OpenCLIP embeddings and a distributed query server into an interactive video retrieval system, with no quantitative evaluation reported.

  3. diveXplore 6.0: ITEC's Interactive Video Exploration System at VBS 2022

    cs.MM 2025-08 unverdicted novelty 3.0 of 10

    diveXplore 6.0 adds one-second shot sampling, alternate map search, OCR, speech-to-text, and temporal search for VBS2022, without a reported evaluation.

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