{"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/fusion-gcn-multimodal-action-recognition","title":"Fusion-GCN: Multimodal Action Recognition using Graph Convolutional Networks","arxiv_id":"2109.12946","date":"2021-09-27","proceeding":null,"authors":["Michael Duhme","Raphael Memmesheimer","Dietrich Paulus"],"abstract":"In this paper, we present Fusion-GCN, an approach for multimodal action recognition using Graph Convolutional Networks (GCNs). Action recognition methods based around GCNs recently yielded state-of-the-art performance for skeleton-based action recognition. With Fusion-GCN, we propose to integrate various sensor data modalities into a graph that is trained using a GCN model for multi-modal action recognition. Additional sensor measurements are incorporated into the graph representation, either on a channel dimension (introducing additional node attributes) or spatial dimension (introducing new nodes). Fusion-GCN was evaluated on two public available datasets, the UTD-MHAD- and MMACT datasets, and demonstrates flexible fusion of RGB sequences, inertial measurements and skeleton sequences. Our approach gets comparable results on the UTD-MHAD dataset and improves the baseline on the large-scale MMACT dataset by a significant margin of up to 12.37% (F1-Measure) with the fusion of skeleton estimates and accelerometer measurements.","url_abs":"https://arxiv.org/abs/2109.12946v1","url_pdf":"https://arxiv.org/pdf/2109.12946v1.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":"fusion-gcn-multimodal-action-recognition","repo_url":"https://github.com/mduhme/fusion-gcn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"multimodal-activity-recognition","task_name":"Multimodal Activity Recognition"},{"task_slug":"skeleton-based-action-recognition","task_name":"Skeleton Based Action Recognition"}],"methods":[{"method_slug":"gcn","method_name":"GCN"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multimodal-activity-recognition-on-mmact","task":"Multimodal Activity Recognition","dataset":"MMAct","model":"Fusion-GCN","rank_in_archive_order":3,"of":3,"metrics":{"F1-Score (Cross-Subject)":"89.60"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2109.12946","atlas_url":"https://app.syntology.ai/?focus=2109.12946","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}