{"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/weakly-supervised-action-learning-with-rnn","title":"Weakly Supervised Action Learning with RNN based Fine-to-coarse Modeling","arxiv_id":"1703.08132","date":"2017-03-23","proceeding":"CVPR 2017 7","authors":["Alexander Richard","Hilde Kuehne","Juergen Gall"],"abstract":"We present an approach for weakly supervised learning of human actions. Given\na set of videos and an ordered list of the occurring actions, the goal is to\ninfer start and end frames of the related action classes within the video and\nto train the respective action classifiers without any need for hand labeled\nframe boundaries. To address this task, we propose a combination of a\ndiscriminative representation of subactions, modeled by a recurrent neural\nnetwork, and a coarse probabilistic model to allow for a temporal alignment and\ninference over long sequences. While this system alone already generates good\nresults, we show that the performance can be further improved by approximating\nthe number of subactions to the characteristics of the different action\nclasses. To this end, we adapt the number of subaction classes by iterating\nrealignment and reestimation during training. The proposed system is evaluated\non two benchmark datasets, the Breakfast and the Hollywood extended dataset,\nshowing a competitive performance on various weak learning tasks such as\ntemporal action segmentation and action alignment.","url_abs":"http://arxiv.org/abs/1703.08132v3","url_pdf":"http://arxiv.org/pdf/1703.08132v3.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":"weakly-supervised-action-learning-with-rnn","repo_url":"https://github.com/alexanderrichard/squirrel","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"action-segmentation","task_name":"Action Segmentation"},{"task_slug":"temporal-action-segmentation","task_name":"Temporal Action Segmentation"},{"task_slug":"weakly-supervised-learning","task_name":"Weakly-supervised Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.08132","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}