{"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/mlgcn-multi-laplacian-graph-convolutional","title":"MLGCN: Multi-Laplacian Graph Convolutional Networks for Human Action Recognition","arxiv_id":null,"date":"2019-09-11","proceeding":"30th British Machine Vision Conference 2019 9","authors":["Ahmed Mazari","Hichem Sahbi"],"abstract":"Convolutional neural networks are nowadays witnessing a major success in different pattern recognition problems. These learning models were basically designed to handle vectorial data such as images but their extension to non-vectorial and semi-structured data (namely graphs with variable sizes, topology, etc.) remains a major challenge, though a few interesting solutions are currently emerging. In this paper, we introduce MLGCN; a novel spectral Multi-Laplacian Graph Convolutional Network. The main ontribution of this method resides in a new design principle that learns graph-laplacians as convex combinations of other elementary \r\nlaplacians – each one dedicated to a particular topology of the input graphs. We also introduce a novel pooling operator, on graphs, that proceeds in two steps: context-dependent node expansion is achieved, followed by a global average pooling; the strength of this two-step process resides in its ability to preserve the discrimination power of nodes while achieving permutation invariance. Experiments conducted on SBU and UCF-101 datasets, show the validity of our method for the challenging task of action recognition. \r\n\r\nSupplementary :  https://bit.ly/2ku2lYv","url_abs":"https://bmvc2019.org/wp-content/uploads/papers/1103-paper.pdf","url_pdf":"https://bmvc2019.org/wp-content/uploads/papers/1103-paper.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":[],"tasks":[{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"skeleton-based-action-recognition","task_name":"Skeleton Based Action Recognition"},{"task_slug":"action-recognition","task_name":"Temporal Action Localization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/action-recognition-in-videos-on-ucf101","task":"Action Recognition","dataset":"UCF101","model":"MLGCN","rank_in_archive_order":86,"of":91,"metrics":{"3-fold Accuracy":"63.27"},"uses_additional_data":false},{"leaderboard":"/sota/skeleton-based-action-recognition-on-sbu","task":"Skeleton Based Action Recognition","dataset":"SBU / SBU-Refine","model":"MLGCN","rank_in_archive_order":2,"of":9,"metrics":{"Accuracy":"98.60%"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}