{"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/infinibench-a-comprehensive-benchmark-for","title":"InfiniBench: A Comprehensive Benchmark for Large Multimodal Models in Very Long Video Understanding","arxiv_id":"2406.19875","date":"2024-06-28","proceeding":null,"authors":["Kirolos Ataallah","Chenhui Gou","Eslam Abdelrahman","Khushbu Pahwa","Jian Ding","Mohamed Elhoseiny"],"abstract":"Understanding long videos, ranging from tens of minutes to several hours, presents unique challenges in video comprehension. Despite the increasing importance of long-form video content, existing benchmarks primarily focus on shorter clips. To address this gap, we introduce InfiniBench a comprehensive benchmark for very long video understanding which presents 1)The longest video duration, averaging 52.59 minutes per video 2) The largest number of question-answer pairs, 108.2K 3) Diversity in questions that examine nine different skills and include both multiple-choice questions and open-ended questions 4) Human-centric, as the video sources come from movies and daily TV shows, with specific human-level question designs such as Movie Spoiler Questions that require critical thinking and comprehensive understanding. Using InfiniBench, we comprehensively evaluate existing Large Multi-Modality Models (LMMs) on each skill, including the commercial models such as GPT-4o and Gemini 1.5 Flash and the open-source models. The evaluation shows significant challenges in our benchmark. Our findings reveal that even leading AI models like GPT-4o and Gemini 1.5 Flash face challenges in achieving high performance in long video understanding, with average accuracies of just 49.16\\% and 42.72\\%, and average scores of 3.22 and 2.71 out of 5, respectively. We hope this benchmark will stimulate the LMMs community towards long video and human-level understanding. Our benchmark can be accessed at https://vision-cair.github.io/InfiniBench/","url_abs":"https://arxiv.org/abs/2406.19875v2","url_pdf":"https://arxiv.org/pdf/2406.19875v2.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":"infinibench-a-comprehensive-benchmark-for","repo_url":"https://github.com/Vision-CAIR/InfiniBench","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"multiple-choice","task_name":"Multiple-choice"},{"task_slug":"video-understanding","task_name":"Video Understanding"}],"methods":[{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[{"slug":"infinibench","name":"InfiniBench","full_name":"InfiniBench: A Comprehensive Benchmark for Large Multimodal Models in Very Long Video Understanding"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2406.19875","atlas_url":"https://app.syntology.ai/?focus=2406.19875","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}