{"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/metasci-scalable-and-adaptive-reconstruction","title":"MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive Sensing","arxiv_id":"2103.01786","date":"2021-03-02","proceeding":"CVPR 2021 1","authors":["Zhengjue Wang","Hao Zhang","Ziheng Cheng","Bo Chen","Xin Yuan"],"abstract":"To capture high-speed videos using a two-dimensional detector, video snapshot compressive imaging (SCI) is a promising system, where the video frames are coded by different masks and then compressed to a snapshot measurement. Following this, efficient algorithms are desired to reconstruct the high-speed frames, where the state-of-the-art results are achieved by deep learning networks. However, these networks are usually trained for specific small-scale masks and often have high demands of training time and GPU memory, which are hence {\\bf \\em not flexible} to $i$) a new mask with the same size and $ii$) a larger-scale mask. We address these challenges by developing a Meta Modulated Convolutional Network for SCI reconstruction, dubbed MetaSCI. MetaSCI is composed of a shared backbone for different masks, and light-weight meta-modulation parameters to evolve to different modulation parameters for each mask, thus having the properties of {\\bf \\em fast adaptation} to new masks (or systems) and ready to {\\bf \\em scale to large data}. Extensive simulation and real data results demonstrate the superior performance of our proposed approach. Our code is available at {\\small\\url{https://github.com/xyvirtualgroup/MetaSCI-CVPR2021}}.","url_abs":"https://arxiv.org/abs/2103.01786v1","url_pdf":"https://arxiv.org/pdf/2103.01786v1.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":"metasci-scalable-and-adaptive-reconstruction","repo_url":"https://github.com/xyvirtualgroup/MetaSCI-CVPR2021","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"metasci-scalable-and-adaptive-reconstruction","repo_url":"https://github.com/Scientific-Research-Algorithm-Toolbox/SCI-algorithms","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"compressive-sensing","task_name":"Compressive Sensing"},{"task_slug":null,"task_name":"GPU"},{"task_slug":"video-compressive-sensing","task_name":"Video Compressive Sensing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2103.01786","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}