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Specifically, video snapshot compressive imaging (SCI), which captures high-speed video using a low-speed camera is considered in this work, in which multiple (\n            <jats:italic>B<\/jats:italic>\n            ) video frames can be reconstructed from a snapshot measurement. One research gap in previous studies is how to adapt\n            <jats:italic>B<\/jats:italic>\n            in the video SCI system for different scenes. In this article, we fill this gap utilizing reinforcement learning (RL). An RL model, as well as various convolutional neural networks for reconstruction, are learned to achieve adaptive sensing of video SCI systems. Furthermore, the performance of an object detection network using directly the video SCI measurements\n            <jats:italic>without reconstruction<\/jats:italic>\n            is also used to perform RL-based adaptive video compressive sensing. Our proposed adaptive SCI method can thus be implemented in low cost and real time. Our work takes the technology one step further towards real applications of video SCI.\n          <\/jats:p>\n          <jats:p\/>","DOI":"10.1145\/3608479","type":"journal-article","created":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T11:55:15Z","timestamp":1689076515000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Reinforcement Learning for Adaptive Video Compressive Sensing"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9846-7570","authenticated-orcid":false,"given":"Sidi","family":"Lu","sequence":"first","affiliation":[{"name":"Department of Computer Science, William &amp; Mary, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8311-7524","authenticated-orcid":false,"given":"Xin","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Engineering, Westlake University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4554-0070","authenticated-orcid":false,"given":"Aggelos K.","family":"Katsaggelos","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Northwestern University, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5864-4675","authenticated-orcid":false,"given":"Weisong","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Sciences, University of Delaware, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,8,11]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"251","volume-title":"Proceedings of the 20th ACM International Symposium on Mobile ad Hoc Networking and Computing","author":"Bhattacharyya Rajarshi","year":"2019","unstructured":"Rajarshi Bhattacharyya, Archana Bura, Desik Rengarajan, Mason Rumuly, Srinivas Shakkottai, Dileep Kalathil, Ricky K. 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