{"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/localizing-moments-in-long-video-via","title":"Localizing Moments in Long Video Via Multimodal Guidance","arxiv_id":"2302.13372","date":"2023-02-26","proceeding":"ICCV 2023 1","authors":["Wayner Barrios","Mattia Soldan","Alberto Mario Ceballos-Arroyo","Fabian Caba Heilbron","Bernard Ghanem"],"abstract":"The recent introduction of the large-scale, long-form MAD and Ego4D datasets has enabled researchers to investigate the performance of current state-of-the-art methods for video grounding in the long-form setup, with interesting findings: current grounding methods alone fail at tackling this challenging task and setup due to their inability to process long video sequences. In this paper, we propose a method for improving the performance of natural language grounding in long videos by identifying and pruning out non-describable windows. We design a guided grounding framework consisting of a Guidance Model and a base grounding model. The Guidance Model emphasizes describable windows, while the base grounding model analyzes short temporal windows to determine which segments accurately match a given language query. We offer two designs for the Guidance Model: Query-Agnostic and Query-Dependent, which balance efficiency and accuracy. Experiments demonstrate that our proposed method outperforms state-of-the-art models by 4.1% in MAD and 4.52% in Ego4D (NLQ), respectively. Code, data and MAD's audio features necessary to reproduce our experiments are available at: https://github.com/waybarrios/guidance-based-video-grounding.","url_abs":"https://arxiv.org/abs/2302.13372v2","url_pdf":"https://arxiv.org/pdf/2302.13372v2.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":"localizing-moments-in-long-video-via","repo_url":"https://github.com/waybarrios/guidance-based-video-grounding","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"natural-language-moment-retrieval","task_name":"Natural Language Moment Retrieval"},{"task_slug":"natural-language-visual-grounding","task_name":"Natural Language Visual Grounding"},{"task_slug":"video-grounding","task_name":"Video Grounding"},{"task_slug":"video-understanding","task_name":"Video Understanding"}],"methods":[{"method_slug":"base","method_name":"BASE"},{"method_slug":"pruning","method_name":"Pruning"},{"method_slug":"fail","method_name":"fail"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/natural-language-moment-retrieval-on-mad","task":"Natural Language Moment Retrieval","dataset":"MAD","model":"Zero-Shot CLIP + Guidance Model","rank_in_archive_order":4,"of":8,"metrics":{"R@1,IoU=0.1":"9.3","R@1,IoU=0.3":"4.65","R@1,IoU=0.5":"2.16","R@10,IoU=0.1":"24.30","R@10,IoU=0.3":"17.73","R@10,IoU=0.5":"11.09","R@100,IoU=0.1":"47.35","R@100,IoU=0.3":"39.58","R@100,IoU=0.5":"29.68","R@5,IoU=0.1":"18.96","R@5,IoU=0.3":"13.06","R@5,IoU=0.5":"7.4","R@50,IoU=0.1":"39.79","R@50,IoU=0.3":"32.23","R@50,IoU=0.5":"23.21"},"uses_additional_data":false},{"leaderboard":"/sota/natural-language-moment-retrieval-on-mad","task":"Natural Language Moment Retrieval","dataset":"MAD","model":"VLG-Net + Guidance Model","rank_in_archive_order":6,"of":8,"metrics":{"R@1,IoU=0.1":"5.60","R@1,IoU=0.3":"4.28","R@1,IoU=0.5":"2.48","R@10,IoU=0.1":"23.64","R@10,IoU=0.3":"19.86","R@10,IoU=0.5":"13.72","R@100,IoU=0.1":"55.59","R@100,IoU=0.3":"49.38","R@100,IoU=0.5":"39.12","R@5,IoU=0.1":"16.07","R@5,IoU=0.5":"8.78","R@50,IoU=0.1":"45.35","R@50,IoU=0.3":"39.77","R@50,IoU=0.5":"30.22"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2302.13372","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}