{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/towards-automatic-learning-of-procedures-from","title":"Towards Automatic Learning of Procedures from Web Instructional Videos","arxiv_id":"1703.09788","date":"2017-03-28","proceeding":null,"authors":["Luowei Zhou","Chenliang Xu","Jason J. Corso"],"abstract":"The potential for agents, whether embodied or software, to learn by observing\nother agents performing procedures involving objects and actions is rich.\nCurrent research on automatic procedure learning heavily relies on action\nlabels or video subtitles, even during the evaluation phase, which makes them\ninfeasible in real-world scenarios. This leads to our question: can the\nhuman-consensus structure of a procedure be learned from a large set of long,\nunconstrained videos (e.g., instructional videos from YouTube) with only visual\nevidence? To answer this question, we introduce the problem of procedure\nsegmentation--to segment a video procedure into category-independent procedure\nsegments. Given that no large-scale dataset is available for this problem, we\ncollect a large-scale procedure segmentation dataset with procedure segments\ntemporally localized and described; we use cooking videos and name the dataset\nYouCook2. We propose a segment-level recurrent network for generating procedure\nsegments by modeling the dependencies across segments. The generated segments\ncan be used as pre-processing for other tasks, such as dense video captioning\nand event parsing. We show in our experiments that the proposed model\noutperforms competitive baselines in procedure segmentation.","url_abs":"http://arxiv.org/abs/1703.09788v3","url_pdf":"http://arxiv.org/pdf/1703.09788v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"towards-automatic-learning-of-procedures-from","repo_url":"https://github.com/LuoweiZhou/ProcNets-YouCook2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"torch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"dense-video-captioning","task_name":"Dense Video Captioning"},{"task_slug":"procedure-learning","task_name":"Procedure Learning"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"video-captioning","task_name":"Video Captioning"}],"methods":[],"datasets_introduced":[{"slug":"youcook2","name":"YouCook2","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.09788","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.09788"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/LuoweiZhou/ProcNets-YouCook2","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":2},"by_repo_kind":{"listed":{"samples":2,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"0ff30cc6f43934dc","entry":"getInfo","repo":"LuoweiZhou/ProcNets-YouCook2","repo_kind":"listed","path":"script/videosample.py","file_url":"https://github.com/LuoweiZhou/ProcNets-YouCook2/blob/HEAD/script/videosample.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"0ff30cc6f43934dc"}},{"code_sha256_prefix":"360fcc8966794164","entry":"yuv_import","repo":"LuoweiZhou/ProcNets-YouCook2","repo_kind":"listed","path":"script/videosample.py","file_url":"https://github.com/LuoweiZhou/ProcNets-YouCook2/blob/HEAD/script/videosample.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"360fcc8966794164"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}