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K. Xu and Ren, F. , “CSVideoNet: A Real-time End-to-end Learning Framework for High-frame-rate Video Compressive Sensing”, IEEE Winter Conference on Applications of Computer Vision (WACV). 2018. (821.05 KB)
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Z. Yu, Trindade, B. Machado, Green, M. , Zhang, Z. , Sneha, P. , Bank-Tavakoli, E. , Pawlowicz, C. , and Ren, F. , “A Data-Driven Approach for Automated Integrated Circuit Segmentation of Scan Electron Microscopy Images”, The 29th IEEE International Conference on Image Processing (ICIP). Bordeaux, France, 2022. (1.03 MB)
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Z. Zhang, Xu, K. , and Ren, F. , “CRA: A Generic Compression Ratio Adaptor for End-to-end Data-driven Image Compressive Sensing Reconstruction Frameworks”, International Conference on Acoustics, Speech, and Signal Processing. Barcelona, Spain, pp. 1740-1749, 2020. (328.74 KB)
X. Zhang, Xu, W. , Huang, M. - C. , Amini, N. , and Ren, F. , “See UV on Your Skin: An Ultraviolet Sensing and Visualization System”, Proceedings of the 8th International Conference on Body Area Networks (BodyNets). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), pp. 22-28, 2013. (1.82 MB)
Z. Zhang, Zhao, J. , and Ren, F. , “An Experimental Study on Transferring Data-Driven Image Compressive Sensing to Bioelectric Signals”, The 47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Singapore, pp. 1191-1195, 2022. (1.29 MB)
X. Zhang, Huang, M. - C. , Ren, F. , Xu, W. , Guan, N. , and Yi, W. , “Proper Running Posture Guide: A Wearable Biomechanics Capture System”, Proceedings of the 8th International Conference on Body Area Networks (BodyNets). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 2013. (1.54 MB)
J. Zhao, Westerham, M. , Lakatos-Toth, M. , Zhang, Z. , Moskoff, A. , and Ren, F. , “OpenICS: Open Image Compressive Sensing Toolbox and Benchmark”, Software Impacts, vol. 9, 2021. (362.26 KB)
X. Zhou, Peng, Y. , Long, C. , Ren, F. , and Shi, C. , “MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time”, The Thirty-seventh International Conference on Machine Learning. Virtual Event, 2020. (9.74 MB)

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