{"url":"/dataset/fivr-200k","name":"FIVR-200K","full_name":null,"description_markdown":"The FIVR-200K dataset has been collected to simulate the problem of Fine-grained Incident Video Retrieval (FIVR). The dataset comprises 225,960 videos associated with 4,687 Wikipedia events and 100 selected video queries.","description_withheld":null,"homepage":"http://ndd.iti.gr/fivr/","introduced_date":"2019-03-17","introduced_date_note":null,"introduced_by":{"paper":"/paper/fivr-fine-grained-incident-video-retrieval","title":"FIVR: Fine-grained Incident Video Retrieval","first_author":"Giorgos Kordopatis-Zilos","url":null},"license":null,"modalities":[{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Video Retrieval","url":"/task/video-retrieval","datasets_with_task":"/datasets/task/video-retrieval"},{"name":"Contrastive Learning","url":"/task/contrastive-learning","datasets_with_task":"/datasets/task/contrastive-learning"}],"languages":[],"variants":["FIVR-200K"],"data_loaders":[],"num_papers_in_archive":16,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/video-retrieval-on-fivr-200k","task":"Video Retrieval","dataset_variant":"FIVR-200K","rows":17,"metrics":["mAP (ISVR)","mAP (CSVR)","mAP (DSVR)"],"first_row_in_archive_order":{"model":"S2VS","paper":"/paper/self-supervised-video-similarity-learning","metrics":{"mAP (CSVR)":"0.879","mAP (DSVR)":"0.927","mAP (ISVR)":"0.746"},"code_links":[{"title":"gkordo/s2vs","url":"https://github.com/gkordo/s2vs"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/self-supervised-video-similarity-learning","title":"Self-Supervised Video Similarity Learning","date":"2023-04-06","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":0,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/vvs-video-to-video-retrieval-with-irrelevant","title":"VVS: Video-to-Video Retrieval with Irrelevant Frame Suppression","date":"2023-03-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/vrag-region-attention-graphs-for-content","title":"VRAG: Region Attention Graphs for Content-Based Video Retrieval","date":"2022-05-18","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/exploring-the-temporal-cues-to-enhance-video","title":"Exploring the Temporal Cues to Enhance Video Retrieval on Standardized CDVA","date":"2022-04-11","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/dns-distill-and-select-for-efficient-and","title":"DnS: Distill-and-Select for Efficient and Accurate Video Indexing and Retrieval","date":"2021-06-24","rows_on_this_dataset":3,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/context-encoding-for-video-retrieval-with","title":"Temporal Context Aggregation for Video Retrieval with Contrastive Learning","date":"2020-08-04","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/visil-fine-grained-spatio-temporal-video","title":"ViSiL: Fine-grained Spatio-Temporal Video Similarity Learning","date":"2019-08-20","rows_on_this_dataset":4,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":0,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/lamv-learning-to-align-and-match-videos-with","title":"LAMV: Learning to Align and Match Videos With Kernelized Temporal Layers","date":"2018-06-01","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":13,"samples_ran":0,"samples_unverified":13,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":3,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}