{"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/videochat-flash-hierarchical-compression-for","title":"VideoChat-Flash: Hierarchical Compression for Long-Context Video Modeling","arxiv_id":"2501.00574","date":"2024-12-31","proceeding":null,"authors":["Xinhao Li","Yi Wang","Jiashuo Yu","Xiangyu Zeng","Yuhan Zhu","Haian Huang","Jianfei Gao","Kunchang Li","Yinan He","Chenting Wang","Yu Qiao","Yali Wang","LiMin Wang"],"abstract":"Long-context modeling is a critical capability for multimodal large language models (MLLMs), enabling them to process long-form contents with implicit memorization. Despite its advances, handling extremely long videos remains challenging due to the difficulty in maintaining crucial features over extended sequences. This paper introduces a Hierarchical visual token Compression (HiCo) method designed for high-fidelity representation and a practical context modeling system VideoChat-Flash tailored for multimodal long-sequence processing. HiCo capitalizes on the redundancy of visual information in long videos to compress long video context from the clip-level to the video-level, reducing the compute significantly while preserving essential details. VideoChat-Flash features a multi-stage short-to-long learning scheme, a rich dataset of real-world long videos named LongVid, and an upgraded \"Needle-In-A-video-Haystack\" (NIAH) for evaluating context capacities. In extensive experiments, VideoChat-Flash shows the leading performance on both mainstream long and short video benchmarks at the 2B and 7B model scale. It firstly gets 99.1% accuracy over 10,000 frames in NIAH among open-source models.","url_abs":"https://arxiv.org/abs/2501.00574v2","url_pdf":"https://arxiv.org/pdf/2501.00574v2.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":"videochat-flash-hierarchical-compression-for","repo_url":"https://github.com/opengvlab/videochat-flash","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"videochat-flash-hierarchical-compression-for","repo_url":"https://github.com/opengvlab/internvideo2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"memorization","task_name":"Memorization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2501.00574","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}