{"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/actionness-estimation-using-hybrid-fully","title":"Actionness Estimation Using Hybrid Fully Convolutional Networks","arxiv_id":"1604.07279","date":"2016-04-25","proceeding":"CVPR 2016 6","authors":["Limin Wang","Yu Qiao","Xiaoou Tang","Luc van Gool"],"abstract":"Actionness was introduced to quantify the likelihood of containing a generic\naction instance at a specific location. Accurate and efficient estimation of\nactionness is important in video analysis and may benefit other relevant tasks\nsuch as action recognition and action detection. This paper presents a new deep\narchitecture for actionness estimation, called hybrid fully convolutional\nnetwork (H-FCN), which is composed of appearance FCN (A-FCN) and motion FCN\n(M-FCN). These two FCNs leverage the strong capacity of deep models to estimate\nactionness maps from the perspectives of static appearance and dynamic motion,\nrespectively. In addition, the fully convolutional nature of H-FCN allows it to\nefficiently process videos with arbitrary sizes. Experiments are conducted on\nthe challenging datasets of Stanford40, UCF Sports, and JHMDB to verify the\neffectiveness of H-FCN on actionness estimation, which demonstrate that our\nmethod achieves superior performance to previous ones. Moreover, we apply the\nestimated actionness maps on action proposal generation and action detection.\nOur actionness maps advance the current state-of-the-art performance of these\ntasks substantially.","url_abs":"http://arxiv.org/abs/1604.07279v1","url_pdf":"http://arxiv.org/pdf/1604.07279v1.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-detection","task_name":"Action Detection"},{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"action-recognition","task_name":"Temporal Action Localization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/action-detection-on-j-hmdb","task":"Action Detection","dataset":"J-HMDB","model":"Actionness","rank_in_archive_order":12,"of":18,"metrics":{"Frame-mAP 0.5":"39.9"},"uses_additional_data":false},{"leaderboard":"/sota/action-detection-on-j-hmdb","task":"Action Detection","dataset":"J-HMDB","model":"Actionnness","rank_in_archive_order":18,"of":18,"metrics":{"Video-mAP 0.5":"56.4"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1604.07279","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}