{"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/applying-cooperative-machine-learning-to","title":"Applying Cooperative Machine Learning to Speed Up the Annotation of Social Signals in Large Multi-modal Corpora","arxiv_id":"1802.02565","date":"2018-02-07","proceeding":null,"authors":["Johannes Wagner","Tobias Baur","Yue Zhang","Michel F. Valstar","Björn Schuller","Elisabeth André"],"abstract":"Scientific disciplines, such as Behavioural Psychology, Anthropology and\nrecently Social Signal Processing are concerned with the systematic exploration\nof human behaviour. A typical work-flow includes the manual annotation (also\ncalled coding) of social signals in multi-modal corpora of considerable size.\nFor the involved annotators this defines an exhausting and time-consuming task.\nIn the article at hand we present a novel method and also provide the tools to\nspeed up the coding procedure. To this end, we suggest and evaluate the use of\nCooperative Machine Learning (CML) techniques to reduce manual labelling\nefforts by combining the power of computational capabilities and human\nintelligence. The proposed CML strategy starts with a small number of labelled\ninstances and concentrates on predicting local parts first. Afterwards, a\nsession-independent classification model is created to finish the remaining\nparts of the database. Confidence values are computed to guide the manual\ninspection and correction of the predictions. To bring the proposed approach\ninto application we introduce NOVA - an open-source tool for collaborative and\nmachine-aided annotations. In particular, it gives labellers immediate access\nto CML strategies and directly provides visual feedback on the results. Our\nexperiments show that the proposed method has the potential to significantly\nreduce human labelling efforts.","url_abs":"http://arxiv.org/abs/1802.02565v1","url_pdf":"http://arxiv.org/pdf/1802.02565v1.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":"applying-cooperative-machine-learning-to","repo_url":"https://github.com/hcmlab/nova","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}