I am a PhD student in the Communications and Signal Processing (CSP) group at Imperial College London, under the supervision of . I received a M.Sc. (2020) and B.Sc (2017) degree in Instrumentation Science and Technology from the Beihang University (Beijing, China). My research interests include event-based vision, model-based deep learning, SNNs and remote sensing image processing.
Our paper "First-spike coding promotes accurate and efficient spiking neural networks for discrete events with rich temporal structures" has been accepted!
Related codes will be released soon!
S. Liu, R. Alexandru and P. L. Dragotti, "Convolutional ISTA Network with Temporal Consistency Constraints for Video Reconstruction from Event Cameras," ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 1935-1939, doi: 10.1109/ICASSP43922.2022.9746331.
Jiang, J., Lyu, C., Liu, S., He, Y., & Hao, X. (2020). RWSNet: a semantic segmentation network based on SegNet combined with random walk for remote sensing. International Journal of Remote Sensing, 41 (2), 487-505. doi: 10.1080/01431161.2019.1643937
Liu S, Jiang J. Registration Algorithm Based on Line-Intersection-Line for Satellite Remote Sensing Images of Urban Areas. Remote Sensing. 2019; 11(12):1400. doi: 10.3390/rs11121400
S. Liu and J. Jiang, "Remote sensing image registration based on feature points of global edge," 2017 IEEE International Conference on Imaging Systems and Techniques (IST), 2017, pp. 1-6, doi: 10.1109/IST.2017.8261564
Oct 2020, Department Scholarship for 3.5-year of PhD study, Department of Electrical and Electronic Engineering, Imperial College London.
July 2020, Excellent Graduate Award, Beijing Municipal Commission of Education.
Dec 2019, National Scholarship, Chinese Ministry of Education.
July 2017, Excellent Graduate Award, Beijing Municipal Commission of Education.
Liu S, Leung VCH, Dragotti PL, 2023, First-spike coding promotes accurate and efficient spiking neural networks for discrete events with rich temporal structures, Frontiers in Neuroscience, ISSN:1662-453X