{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T02:26:53Z","timestamp":1783132013490,"version":"3.54.6"},"reference-count":0,"publisher":"SPIE-Intl Soc Optical Eng","issue":"04","funder":[{"name":"Open Project of Tianjin Key Laboratory of Optoelectronic Detection Technology and System","award":["2025LODTS111"],"award-info":[{"award-number":["2025LODTS111"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Electron. Imag."],"published-print":{"date-parts":[[2026,7,4]]},"abstract":"<jats:p>Event-based optical-flow estimation remains challenging across different fixed-frequency sampling, particularly under low-frequency conditions where observations are sparse and motion displacements are large. Conventional methods based on convolutional neural networks (CNNs) and recurrent neural networks (RNNs) update their states at fixed discrete-time steps, ignoring the actual temporal intervals between events, which can lead to inconsistent temporal modeling and reduced accuracy. To address these challenges, we propose TSR-SSM, a temporal scale-robust state-space model for event-based optical-flow estimation. TSR-SSM discretizes the state space according to the temporal interval of each fixed sampling setting, enabling consistent temporal modeling under each fixed sampling setting. In addition, we design an SSM-enhanced cascaded decoder that integrates spatial state-space modeling with stability regularization, capturing long-range spatial dependencies while suppressing high-frequency noise. Experiments on the HREM and DSEC datasets show that TSR-SSM consistently outperforms prior methods, achieving a 4.2% reduction in average endpoint error under standard sampling and a 9.7% reduction under low-frequency conditions while maintaining real-time inference speed at 37.46 FPS on DSEC. These results demonstrate that TSR-SSM enables accurate, robust, and efficient event-based optical-flow estimation across diverse temporal scales.<\/jats:p>","DOI":"10.1117\/1.jei.35.4.043002","type":"journal-article","created":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T02:13:58Z","timestamp":1783131238000},"source":"Crossref","is-referenced-by-count":0,"title":["TSR-SSM: temporal scale-robust state-space models for efficient event optical flow"],"prefix":"10.1117","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2685-4437","authenticated-orcid":false,"given":"Jianming","family":"Wang","sequence":"additional","affiliation":[{"name":"Tiangong University, School of Computer Science and Technology, Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, Tianjin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8886-6137","authenticated-orcid":false,"given":"Yukuan","family":"Sun","sequence":"additional","affiliation":[{"name":"Tiangong University, Center for Engineering Internship and Training, Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, Tianjin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"189","container-title":["Journal of Electronic Imaging"],"original-title":[],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T02:13:58Z","timestamp":1783131238000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.spiedigitallibrary.org\/journals\/journal-of-electronic-imaging\/volume-35\/issue-04\/043002\/TSR-SSM--temporal-scale-robust-state-space-models-for\/10.1117\/1.JEI.35.4.043002.full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,4]]},"references-count":0,"journal-issue":{"issue":"04","published-online":{"date-parts":[[2026,7,1]]}},"URL":"https:\/\/doi.org\/10.1117\/1.jei.35.4.043002","relation":{},"ISSN":["1017-9909"],"issn-type":[{"value":"1017-9909","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7,4]]}}}