Cerru02/FIVR-5K-MTEB

FIVR-5K for MTEB

This repository packages the metadata needed for the MTEB/MOEB version of fine-grained incident video retrieval. It contains no video bytes.

Relationship to the source benchmarks

FIVR-200K is the authoritative source of the YouTube IDs and human ND/DS/CS/IS annotations. The canonical FIVR-5K protocol published with ViSiL selects the 50 most difficult DSVR queries (using iMAC to measure difficulty), randomly retains 30% of annotated videos per label category for each query, and publishes a 5,000-video database from FIVR-200K. The released ViSiL metadata is used to validate membership; the MTEB construction does not rerun that historical random selection.

VideoEval freezes a downloadable subset of that established protocol: 31 of the canonical queries, 3,415 canonical database rows, and 3,445 unique IDs (one query is also a database row). Its annotations are identical to the official FIVR-200K JSON. The MTEB construction validates all of these relationships instead of sampling a new subset.

The older ViSiL pickle and current FIVR/VideoEval annotations differ for two manifest items, both historical IS positives that are absent from the current official annotation. The construction records and validates this drift, then uses the current official annotation bundled identically by VideoEval.

The MTEB freeze re-audited all VideoEval IDs on 2026-08-10. It observed 3217 available and 228 unavailable/restricted IDs. After removing unavailable media, excluding the single query self-match from the corpus, and retaining only available queries with a surviving DSVR positive, the evaluation has 29 queries and 3188 corpus videos. Across all 31 VideoEval source queries, unavailable corpus media removed 1 ND, 23 DS, 14 CS, and 22 IS positive assignments. For the 29 retained evaluation queries, the corresponding losses are 1 ND, 21 DS, 8 CS, and 21 IS. The per-query records remain available in positive-losses rather than being silently discarded.

Retrieval definitions

All qrels are binary, exactly following the official evaluator:

• DSVR: ND + DS (417 qrels)
• CSVR: ND + DS + CS (485 qrels)
• ISVR: ND + DS + CS + IS (700 qrels)

The source metric is full-ranking mean average precision. MTEB evaluates map_at_<corpus size>, which is equivalent because every corpus item is ranked.

Media and licensing

The FIVR and ViSiL repositories, the VideoEval metadata repository, and this metadata-only derivative are Apache-2.0. That license does not grant rights to redistribute the underlying third-party YouTube videos. VideoEval likewise instructs users to download original media separately because of potential copyright issues.

Consequently, this repository publishes only IDs, original-source URLs, availability decisions, and qrels. MTEB downloads retained videos from their original URLs into a local cache on first use. A later source disappearance is reported as an error; the benchmark never silently changes its frozen corpus.

Users are responsible for complying with the original platforms' terms and applicable law. Individual media rights remain with their respective owners.

Configurations

• corpus, queries: identifiers, source URLs, frozen availability status, and duration metadata;
• dsvr-qrels, csvr-qrels, isvr-qrels: official binary relevance unions;
• availability: every ID from VideoEval's frozen manifest, including missing items;
• positive-losses: lost positives per query and ND/DS/CS/IS label;
• query-decisions: retained/dropped query decisions.

The audit/ directory contains the human-readable construction summary and the full frozen availability/loss records.

Citations

@article{kordopatis2019fivr,
  author = {Kordopatis-Zilos, Giorgos and Papadopoulos, Symeon and Patras, Ioannis and Kompatsiaris, Ioannis},
  journal = {IEEE Transactions on Multimedia},
  title = {FIVR: Fine-grained Incident Video Retrieval},
  year = {2019}
}

@inproceedings{kordopatis2019visil,
  author = {Kordopatis-Zilos, Giorgos and Papadopoulos, Symeon and Patras, Ioannis and Kompatsiaris, Ioannis},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
  title = {ViSiL: Fine-grained Spatio-Temporal Video Similarity Learning},
  year = {2019}
}

@article{li2024videoeval,
  author = {Li, Xinhao and Huang, Zhenpeng and Wang, Jing and Li, Kunchang and Wang, Limin},
  journal = {arXiv preprint arXiv:2407.06491},
  title = {VideoEval: Comprehensive Benchmark Suite for Low-cost Evaluation of Video Foundation Model},
  year = {2024}
}