{"url":"/dataset/rareact","name":"RareAct","full_name":null,"description_markdown":"**RareAct** is a video dataset of unusual actions, including actions like “blend phone”, “cut keyboard” and “microwave shoes”. It aims at evaluating the zero-shot and few-shot compositionality of action recognition models for unlikely compositions of common action verbs and object nouns. It contains 122 different actions which were obtained by combining verbs and nouns rarely co-occurring together in the large-scale textual corpus from HowTo100M, but that frequently appear separately.\n\nSource: [https://github.com/antoine77340/RareAct](https://github.com/antoine77340/RareAct)\nImage Source: [https://github.com/antoine77340/RareAct](https://github.com/antoine77340/RareAct)","description_withheld":null,"homepage":"https://github.com/antoine77340/RareAct","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/rareact-a-video-dataset-of-unusual","title":"RareAct: A video dataset of unusual interactions","first_author":"Antoine Miech","url":null},"license":null,"modalities":[{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Action Recognition","url":"/task/action-recognition-in-videos","datasets_with_task":"/datasets/task/action-recognition-in-videos"}],"languages":[],"variants":["RareAct"],"data_loaders":[{"repo":"https://github.com/antoine77340/RareAct","url":"https://github.com/antoine77340/RareAct","frameworks":[]}],"num_papers_in_archive":12,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/action-recognition-on-rareact","task":"Action Recognition","dataset_variant":"RareAct","rows":3,"metrics":["mWAP"],"first_row_in_archive_order":{"model":"🦩 Flamingo","paper":"/paper/flamingo-a-visual-language-model-for-few-shot-1","metrics":{"mWAP":"60.8"},"code_links":[{"title":"mlfoundations/open_flamingo","url":"https://github.com/mlfoundations/open_flamingo"},{"title":"lucidrains/flamingo-pytorch","url":"https://github.com/lucidrains/flamingo-pytorch"},{"title":"unispac/visual-adversarial-examples-jailbreak-large-language-models","url":"https://github.com/unispac/visual-adversarial-examples-jailbreak-large-language-models"},{"title":"doc-doc/NExT-OE","url":"https://github.com/doc-doc/NExT-OE"},{"title":"happen2me/cross-gnn","url":"https://github.com/happen2me/cross-gnn"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/flamingo-a-visual-language-model-for-few-shot-1","title":"Flamingo: a Visual Language Model for Few-Shot Learning","date":"2022-04-29","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":24,"samples_ran":18,"samples_unverified":6,"pointer_only_for_licence":7,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/learning-transferable-visual-models-from","title":"Learning Transferable Visual Models From Natural Language Supervision","date":"2021-02-26","rows_on_this_dataset":1,"code_links":82,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":20,"samples_ran":16,"samples_unverified":4,"pointer_only_for_licence":16,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/end-to-end-learning-of-visual-representations","title":"End-to-End Learning of Visual Representations from Uncurated Instructional Videos","date":"2019-12-13","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":3,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":49,"samples_ran":37,"samples_unverified":12,"pointer_only_for_licence":23,"papers_with_no_sample_that_ran":0,"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."}