{"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/fifa-fast-inference-approximation-for-action","title":"FIFA: Fast Inference Approximation for Action Segmentation","arxiv_id":"2108.03894","date":"2021-08-09","proceeding":null,"authors":["Yaser Souri","Yazan Abu Farha","Fabien Despinoy","Gianpiero Francesca","Juergen Gall"],"abstract":"We introduce FIFA, a fast approximate inference method for action segmentation and alignment. Unlike previous approaches, FIFA does not rely on expensive dynamic programming for inference. Instead, it uses an approximate differentiable energy function that can be minimized using gradient-descent. FIFA is a general approach that can replace exact inference improving its speed by more than 5 times while maintaining its performance. FIFA is an anytime inference algorithm that provides a better speed vs. accuracy trade-off compared to exact inference. We apply FIFA on top of state-of-the-art approaches for weakly supervised action segmentation and alignment as well as fully supervised action segmentation. FIFA achieves state-of-the-art results on most metrics on two action segmentation datasets.","url_abs":"https://arxiv.org/abs/2108.03894v1","url_pdf":"https://arxiv.org/pdf/2108.03894v1.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-segmentation","task_name":"Action Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"weakly-supervised-action-segmentation","task_name":"Weakly Supervised Action Segmentation (Transcript)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/action-segmentation-on-breakfast-1","task":"Action Segmentation","dataset":"Breakfast","model":"FIFA + MS-TCN","rank_in_archive_order":17,"of":37,"metrics":{"Acc":"68.6","Average F1":"66.8","Edit":"78.5","F1@10%":"75.5","F1@25%":"70.2","F1@50%":"54.8"},"uses_additional_data":false},{"leaderboard":"/sota/weakly-supervised-action-segmentation","task":"Weakly Supervised Action Segmentation (Transcript)","dataset":"Breakfast","model":"FIFA + MuCon","rank_in_archive_order":2,"of":7,"metrics":{"Acc":"51.3"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2108.03894","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}