{"type":"ScholarlyArticle","title":"SCVBench: A Benchmark with Multi-turn Dialogues for Story-Centric Video Understanding","authors":[{"name":"Sisi You","slug":"sisi-you","given":"Sisi","family":"You","affiliation":""},{"name":"Bowen Yuan","slug":"bowen-yuan","given":"Bowen","family":"Yuan","affiliation":""},{"name":"Bing-Kun Bao","slug":"bing-kun-bao","given":"Bing-Kun","family":"Bao","affiliation":""}],"abstract":"Video understanding seeks to enable machines to interpret visual content across three levels: action, event, and story.  Existing models are limited in their ability to perform high-level long-term story understanding, due to (1) the oversimplified treatment of temporal information and (2) the training bias introduced by action/event-centric datasets. To address this, we introduce SCVBench, a novel benchmark for story-centric video understanding. SCVBench evaluates LVLMs through an event ordering task decomposed into sub-questions leading to a final question, quantitatively measuring historical dialogue exploration. We collected 1,253 final questions and 6,027 sub-question pairs from 925 videos, constructing continuous multi-turn dialogues. Experimental results show that while closed-source GPT-4o outperforms other models, most open-source LVLMs struggle with story-centric video understanding. Additionally, our StoryCoT model significantly surpasses open-source LVLMs on SCVBench. SCVBench aims to advance research by comprehensively analyzing LVLMs' temporal reasoning and comprehension capabilities. Code can be accessed at https://github.com/yuanrr/SCVBench.","doi":"10.24963/ijcai.2025/255","url":"https://ijcai.org/proceedings/2025/255","pdf":"https://ijcai.org/proceedings/2025/0255.pdf","markdown":"https://ijcai.org/proceedings/2025/255.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/255","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Computer Vision","track":"Main Track","number":255,"pages":{"first":2287,"last":2295},"keywords":["Computer Vision: CV: Multimodal learning","Computer Vision: CV: Vision, language and reasoning","Computer Vision: CV: Video analysis and understanding"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}