{"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/online-video-understanding-a-comprehensive","title":"Online Video Understanding: OVBench and VideoChat-Online","arxiv_id":"2501.00584","date":"2024-12-31","proceeding":"CVPR 2025 1","authors":["Zhenpeng Huang","Xinhao Li","Jiaqi Li","Jing Wang","Xiangyu Zeng","Cheng Liang","Tao Wu","Xi Chen","Liang Li","LiMin Wang"],"abstract":"Multimodal Large Language Models (MLLMs) have significantly progressed in offline video understanding. However, applying these models to real-world scenarios, such as autonomous driving and human-computer interaction, presents unique challenges due to the need for real-time processing of continuous online video streams. To this end, this paper presents systematic efforts from three perspectives: evaluation benchmark, model architecture, and training strategy. First, we introduce OVBench, a comprehensive question-answering benchmark designed to evaluate models' ability to perceive, memorize, and reason within online video contexts. It features 6 core task types across three temporal contexts-past, current, and future-forming 16 subtasks from diverse datasets. Second, we propose a new Pyramid Memory Bank (PMB) that effectively retains key spatiotemporal information in video streams. Third, we proposed an offline-to-online learning paradigm, designing an interleaved dialogue format for online video data and constructing an instruction-tuning dataset tailored for online video training. This framework led to the development of VideoChat-Online, a robust and efficient model for online video understanding. Despite the lower computational cost and higher efficiency, VideoChat-Online outperforms existing state-of-the-art offline and online models across popular offline video benchmarks and OVBench, demonstrating the effectiveness of our model architecture and training strategy. % Our approach surpasses existing state-of-the-art offline models Qwen2-VL 7B and online models Flash-VStream, by 4.19% and 23.7% on OVBench, respectively.","url_abs":"https://arxiv.org/abs/2501.00584v2","url_pdf":"https://arxiv.org/pdf/2501.00584v2.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":"online-video-understanding-a-comprehensive","repo_url":"https://github.com/MCG-NJU/VideoChat-Online","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"video-question-answering","task_name":"Video Question Answering"},{"task_slug":"video-understanding","task_name":"Video Understanding"}],"methods":[],"datasets_introduced":[{"slug":"ovbench","name":"OVBench","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-question-answering-on-ovbench","task":"Video Question Answering","dataset":"OVBench","model":"VideoChat-Online (4B)","rank_in_archive_order":2,"of":16,"metrics":{"AVG":"54.9"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2501.00584","atlas_url":"https://app.syntology.ai/?focus=2501.00584","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}