{"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/m2cai-workflow-challenge-convolutional-neural","title":"M2CAI Workflow Challenge: Convolutional Neural Networks with Time Smoothing and Hidden Markov Model for Video Frames Classification","arxiv_id":"1610.05541","date":"2016-10-18","proceeding":null,"authors":["Rémi Cadène","Thomas Robert","Nicolas Thome","Matthieu Cord"],"abstract":"Our approach is among the three best to tackle the M2CAI Workflow challenge.\nThe latter consists in recognizing the operation phase for each frames of\nendoscopic videos. In this technical report, we compare several classification\nmodels and temporal smoothing methods. Our submitted solution is a fine tuned\nResidual Network-200 on 80% of the training set with temporal smoothing using\nsimple temporal averaging of the predictions and a Hidden Markov Model modeling\nthe sequence.","url_abs":"http://arxiv.org/abs/1610.05541v2","url_pdf":"http://arxiv.org/pdf/1610.05541v2.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":"m2cai-workflow-challenge-convolutional-neural","repo_url":"https://github.com/Cadene/torchnet-m2caiworkflow","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"torch","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}