{"url":"/dataset/youtube-inria-instructional","name":"Youtube INRIA Instructional","full_name":"Unsupervised learning from narrated instruction videos","description_markdown":"We address the problem of automatically learning the main steps to complete a certain task, such as changing a car tire, from a set of narrated instruction videos. The contributions of this paper are three-fold. First, we develop a new unsupervised learning approach that takes advantage of the complementary nature of the input video and the associated narration. The method solves two clustering problems, one in text and one in video, applied one after each other and linked by joint constraints to obtain a single coherent sequence of steps in both modalities. Second, we collect and annotate a new challenging dataset of real-world instruction videos from the Internet. The dataset contains about 800,000 frames for five different tasks (How to : change a car tire, perform CardioPulmonary resuscitation (CPR), jump cars, repot a plant and make coffee) that include complex interactions between people and objects, and are captured in a variety of indoor and outdoor settings. Third, we experimentally demonstrate that the proposed method can automatically discover, in an unsupervised manner , the main steps to achieve the task and locate the steps in the input videos.\r\n\r\nThis video presents our results of automatically discovering the scenario for the two following task : changing a tire and performing CardioPulmonary Resuscitation (CPR). At the bottom of the videos, there are three bars. The first one corresponds to our ground truth annotation. The second one corresponds to our time interval prediction in video. Finally the third one corresponds to the constraints that we obtain from the text domain. On the right, there is a list of label. They corresponds to the label recovered by our NLP method in an unsupervised manner.","description_withheld":null,"homepage":"https://www.di.ens.fr/willow/research/instructionvideos/","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Action Segmentation","url":"/task/action-segmentation","datasets_with_task":"/datasets/task/action-segmentation"},{"name":"Unsupervised Action Segmentation","url":"/task/unsupervised-action-segmentation","datasets_with_task":"/datasets/task/unsupervised-action-segmentation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Youtube INRIA Instructional"],"data_loaders":[],"num_papers_in_archive":8,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/unsupervised-action-segmentation-on-youtube","task":"Unsupervised Action Segmentation","dataset_variant":"Youtube INRIA Instructional","rows":8,"metrics":["F1","Acc","Precision","Recall","mIoU"],"first_row_in_archive_order":{"model":"LSTM+AL","paper":"/paper/a-perceptual-prediction-framework-for-self","metrics":{"F1":"39.7"},"code_links":[{"title":"CVPRUSFTampa/EventSegmentation","url":"https://github.com/CVPRUSFTampa/EventSegmentation"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/action-segmentation-on-youtube-inria","task":"Action Segmentation","dataset_variant":"Youtube INRIA Instructional","rows":2,"metrics":["Acc","F1"],"first_row_in_archive_order":{"model":"TSA (FINCH)","paper":"/paper/leveraging-triplet-loss-for-unsupervised","metrics":{"Acc":"62.4","F1":"54.7"},"code_links":[{"title":"elenabbbuenob/tsa-actionseg","url":"https://github.com/elenabbbuenob/tsa-actionseg"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/hierarchical-vector-quantization-for","title":"Hierarchical Vector Quantization for Unsupervised Action Segmentation","date":"2024-12-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/temporally-consistent-unbalanced-optimal","title":"Temporally Consistent Unbalanced Optimal Transport for Unsupervised Action Segmentation","date":"2024-04-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/permutation-aware-action-segmentation-via","title":"Permutation-Aware Action Segmentation via Unsupervised Frame-to-Segment Alignment","date":"2023-05-31","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/leveraging-triplet-loss-for-unsupervised","title":"Leveraging triplet loss for unsupervised action segmentation","date":"2023-04-13","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/unsupervised-activity-segmentation-by-joint","title":"Unsupervised Action Segmentation by Joint Representation Learning and Online Clustering","date":"2021-05-27","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":0,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/action-shuffle-alternating-learning-for","title":"Action Shuffle Alternating Learning for Unsupervised Action Segmentation","date":"2021-04-05","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/unsupervised-learning-of-action-classes-with","title":"Unsupervised learning of action classes with continuous temporal embedding","date":"2019-04-08","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/a-perceptual-prediction-framework-for-self","title":"A Perceptual Prediction Framework for Self Supervised Event Segmentation","date":"2018-11-12","rows_on_this_dataset":1,"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."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":2,"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."}