{"url":"/dataset/egoprocel","name":"EgoProceL","full_name":null,"description_markdown":"EgoProceL is a large-scale dataset for procedure learning. It consists of 62 hours of egocentric videos recorded by 130 subjects performing 16 tasks for procedure learning. EgoProceL contains videos and key-step annotations for multiple tasks from CMU-MMAC, EGTEA Gaze+, and individual tasks like toy-bike assembly, tent assembly, PC assembly, and PC disassembly. EgoProceL overcomes the limitations of third-person videos. As, using third-person videos makes the manipulated object small in appearance and often occluded by the actor, leading to significant errors. In contrast, we observe that videos obtained from first-person (egocentric) wearable cameras provide an unobstructed and clear view of the action.","description_withheld":null,"homepage":"https://sid2697.github.io/egoprocel/","introduced_date":"2022-07-22","introduced_date_note":null,"introduced_by":{"paper":"/paper/my-view-is-the-best-view-procedure-learning","title":"My View is the Best View: Procedure Learning from Egocentric Videos","first_author":"Siddhant Bansal","url":null},"license":null,"modalities":[{"name":"Videos","url":"/datasets/modality/videos"},{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Action Detection","url":"/task/action-detection","datasets_with_task":"/datasets/task/action-detection"},{"name":"Action Segmentation","url":"/task/action-segmentation","datasets_with_task":"/datasets/task/action-segmentation"},{"name":"Video Segmentation","url":"/task/video-segmentation","datasets_with_task":"/datasets/task/video-segmentation"},{"name":"Event Segmentation","url":"/task/event-segmentation","datasets_with_task":"/datasets/task/event-segmentation"},{"name":"Weakly Supervised Action Segmentation (Transcript)","url":"/task/weakly-supervised-action-segmentation","datasets_with_task":"/datasets/task/weakly-supervised-action-segmentation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["EgoProceL"],"data_loaders":[{"repo":"https://github.com/Sid2697/EgoProceL-egocentric-procedure-learning","url":"https://github.com/Sid2697/EgoProceL-egocentric-procedure-learning","frameworks":["pytorch"]}],"num_papers_in_archive":9,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"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."}