Research

N. Y. Wang et al., "Improving the Intelligibility of Speech for Simulated Electric and Acoustic Stimulation Using Fully Convolutional Neural Networks", IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, 2021, pp. 184-195.

T. Chen et al., "Orientation-Aware Vehicle Re-Identification with Semantics-Guided Part Attention Network", in Computer Vision – ECCV 2020, A. Vedaldi et al., Eds., Cham: Springer International Publishing, pp. 330–346.

T. Hsieh et al., "WaveCRN: An Efficient Convolutional Recurrent Neural Network for End-to-End Speech Enhancement", IEEE Signal Processing Letters, vol. 27, 2020, pp. 2149-2153.

C. Yu et al., "Speech Enhancement Based on Denoising Autoencoder With Multi-Branched Encoders", IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 28, 2020, pp. 2756-2769.

C. Wu et al., "Space-Time Guided Association Learning For Unsupervised Person Re-Identification", in 2020 IEEE International Conference on Image Processing (ICIP), pp. 2261-2265.

R. Tseng et al., "A Study of Joint Effect on Denoising Techniques and Visual Cues to Improve Speech Intelligibility in Cochlear Implant Simulation", IEEE Transactions on Cognitive and Developmental Systems, 2020, pp. 1-1.

T. Chen et al., "Viewpoint-aware Channel-wise Attentive Network for Vehicle Re-identification", in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 2448-2455.

J. P. Klopp, L. Chen and S. Chien, "Utilising Low Complexity CNNs to Lift Non-Local Redundancies in Video Coding", IEEE Transactions on Image Processing, vol. 29, 2020, pp. 6372-6385.

C. Liu et al., "Spatially and Temporally Efficient Non-local Attention Network for Video-based Person Re-Identification", CoRR, vol. abs/1908.01683, 2019.

J. Wu et al., "Increasing compactness of deep learning based speech enhancement models with parameter pruning and quantization techniques", IEEE Signal Processing Letters, vol. 26, no. 12, 2019, pp. 1887–1891.