Journal Papers

• G.L. Chen and C.C. Hsu*, “Jointly Defending DeepFake Manipulation and Adversarial Attack using Decoy Mechanism,” IEEE Transactions on Pattern Analysis and Machine Intelligence (IF: 24.31, 2/276=1%), to appear.
• C.H. Lin, Y. Liu, C.Y. Chi, C.C. Hsu, H. Ren, & T.Q. Quek, “Hyperspectral Tensor Completion Using Low-Rank Modeling and Convex Functional Analysis,” IEEE Transactions on Neural Networks and Learning Systems (IF:14.225, 5/276=2%), to appear.
• J.Y. Yang, T.C. Lee, W.T. Liao, C.C. Hsu*, “Multi-Head Self-Attention Mechanism Enabled Individualized Hemoglobin Prediction and Treatment Recommendation Systems in Anemia Management for Hemodialysis Patients,” in Heliyon (IF: 2.85, Rank: 27/138=19% Q1), (accepted)
• C.C. Hsu, et al., “Deep Learning-based Vehicle Trajectory Prediction based on Generative Adversarial Network for Autonomous Driving Applications,” Multimedia Tools and Applications (IF: 2.77, Rank 32/137=23% Q1), to appear. 
• C.Y. Wei, W.Y. Huang, C.Y. Jian, C.C.H. Hsu, C.C. Hsu, et al., “Semantic segmentation guided detector for segmentation, classification, and lesion mapping of acute ischemic stroke in MRI images,” in NeuroImage: Clinical (IF: 4.88, Rank 29/288=10%), (accepted)
• H. Fang, F. Li, H. Fu, X. Sun, X. Cao, F. Lin, J. Son, S. Kim, G. Quellec, S. Matta, S. M, Shankaranarayana, Y.T. Chen, C.H. Wang, N. A Shah, C.Y. Yen Lee, C.-C. Hsu et al., “ADAM Challenge: Detecting Age-related Macular Degeneration from Fundus Images,” in IEEE Transactions on Medical Imaging (IF: 10.05, Rank=21/693=3%), doi: 10.1109/TMI.2022.3172773, May 2022.
• C.C. Hsu, et al., “A Comprehensive Study of Age-related Macular Degeneration Detection,” Multimedia Tools and Applications (IF: 2.75, Rank 32/137=23% Q1), 81.9, 11897-11916, 2022.
• C.C. Hsu, C. -H. Lin, C. -H. Kao and Y. -C. Lin, “DCSN: Deep Compressed Sensing Network for Efficient Hyperspectral Data Transmission of Miniaturized Satellite,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 9, pp. 7773-7789, Sept. 2021. (IF: 5.85, Rank 29/1409=2% in Electrical and Electronic Engineering)
• C.C. Hsu, Y. X. Zhuang, and C. Y. Lee, “Deep Fake Image Detection Based on Pairwise Learning,” Appl Sci-Basel, vol. 10, no. 1, p. 370, Jan 2020. (IF: 2.47, Rank 41/275 in General Engineering)
• C.C. Hsu, C. W. Lin, W. T. Su, and G. Cheung, “SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination,” IEEE Trans Image Process, vol. 28, no. 12, pp. 6225-6236, Dec 2019. (IF:9.34, Rank: 5/334=1% in Computer Graphics and Computer-Aided Design Software).
• C.Y. Lee, G.-L. Chen, Z.-X. Zhang, Y.-H. Chou, and C.C. Hsu*, “Is intensity inhomogeneity correction useful for classification of breast cancer in sonograms using deep neural network?,” Journal of healthcare engineering, vol. 2018, 2018. (*corresponding author) (IF:1.295, Rank:51/150)
• C. Hsu and C.-W. Lin, “Cnn-based joint clustering and representation learning with feature drift compensation for large-scale image data,” IEEE Transactions on Multimedia, vol. 20, no. 2, pp. 421-429, 2017. (IF:6.051, Rank: 2/195=1% in Media Technology).
• C. Hsu, L. Kang, and C. Lin, “Temporally Coherent Super-Resolution of Textured Video via Dynamic Texture Synthesis,” IEEE Transactions on Image Processing, vol. 24, no. 3, pp. 919 – 931, 2015. (IF:9.34, Rank: 5/334=1% in Computer Graphics and Computer-Aided Design Software).
• L.W. Kang, C.C. Hsu, B. Q. Zhuang, C. W. Lin, and C. H. Yeh, “Learning-Based Joint Super-Resolution and Deblocking for a Highly Compressed Image,” Ieee Transactions on Multimedia, vol. 17, no. 7, pp. 921-934, Jul 2015. (IF:6.051, Rank: 2/195=1% in Media Technology).
• C.C. Hsu, C. W. Lin, Y. M. Fang, and W. S. Lin, “Objective Quality Assessment for Image Retargeting Based on Perceptual Geometric Distortion and Information Loss,” Ieee Journal of Selected Topics in Signal Processing, vol. 8, no. 3, pp. 377-389, Jun 2014. (IF:6.688, Rank: 7/543=1% in Signal Processing).
• H.T. Chang, C.C. Hsu, C. H. Yeh, and D. F. Shen, “Image authentication with tampering localization based on watermark embedding in wavelet domain,” Optical Engineering, vol. 48, no. 5, p. 057002, May 2009. (IF:1.21, Rank:93/264)

Book Chapters

• T. Chang and C.C. Hsu, “Fractal-based Secured multiple-image distribution and compression,” in Handbook of Research on Secure Multimedia Distribution, Chapter 27, Edited by Dr. Shiguo Lian and Dr. Yan Zhang, ISBN: 978-1-60566-262-6, 2008.

Conference Papers

• C. C. Hsu, Y. Z. Jiang, and W. H. Huang, “Swift Concurrent Semantic Segmentation and Object Detection on Edge Devices,” IEEE International Conference on Multimedia and Expo (ICME) Workshop, Brisbane, Australia, 10-14 July 2023.
• C. C. Hsu, C.Y. Jian, C.M. Lee, C.H. Tsai, and S.C. Tai, “Bag of Tricks of Hybrid Network for COVID-19 Detection of CT Scans,” IEEE International Conference on Acoustics, Speech, & Signal Processing (ICASSP), June 2023.
• C. C. Hsu and M.Z. Ke, “Seeing is NOT Believing: Toward Forgery Detection for Hyperspectral Image,” IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 2023.
• C.C. Hsu, C.H. Tsai, G.L. Chen, S.D. Ma, and S.C. Dai, “Spatial-Slice Feature Learning using Visual Transformer and Essential Slices Selection Module for COVID-19 Detection of CT Scans in the Wild,” European Conference on Computer Vision (ECCV) Workshop, 23-27 Oct. 2022.
• C.C. Hsu, P.J. Tsai, T.C. Yeh, and X.U. Hou, “A Comprehensive Study of Spatiotemporal Feature Learning for Social Medial Popularity Prediction,” 30th ACM international conference on Multimedia, 10-14 Oct. 2022.
• C.C. Hsu and S.D. Ma, “Few-Shot Semantic Segmentation based on Detail-Preserving-Aware Loss,” in Prof. of IEEE International Conference on Consumer Electronics – Taiwan, Taiwan, July 6-8 2022.
• C.C. Hsu, C. Lee, S. Tai, and Y. Jiang, “Augmented-Training-Aware Bisenet for Real-Time Semantic Segmentation,” in 2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), Taipei City, Taiwan, 2022 pp. 1-6.
• C.C. Hsu, S.J. Dai, and S.N. Chen, “COVID-19 Infection Percentage Estimation via Boosted Hierarchical Vision Transformer,” in Proc. of 21st International Conference on Image Analysis and Processing, LECCE, ITALY, May 23-27 2022.
• B.S. Huang, C.C. Hsu, W.T. Liao, H.Y. Kao, and X.Y. Wang, “DCSN: Deformable Convolutional Semantic Segmentation Neural Network for Non-Rigid Scenes,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 22-27 May 2022.
• G.L. Chen, C.C. Hsu, and M.H. Wu, “Adaptive Distribution Learning with Statistical Hypothesis Testing for COVID-19 CT Scan Classification,” in Proc. of IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), Oct. 2021.
• CH Yeh, D Chiu, LW Kang, CC Hsu, C Lo, ” Generative Adversarial Networks-based Face Hallucination with Identity-Preserving,” in Proc. of IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), 2021.
• C.C. Hsu, C, Lee, C. Lin, K.M. Hung, Y.L. Lin, X.Y. Wang, “Efficient-ROD: Efficient Radar Object Detection based on Densely Connected Residual Network,” in Proc. of ACM International Conference on Multimedia Retrieval (ICMR), 21-24 Aug., 2021.
• Ignatov, C.-M. Chiang, H.-K. Kuo, A. Sycheva, and R. Timofte, “Learned smartphone isp on mobile npus with deep learning, mobile ai 2021 challenge: Report,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 2503-2514.
• -C. Hsu, W.-H. Tseng, H.-T. Yang, C.-H. Lin, and C.-H. Kao, “Rethinking Relation between Model Stacking and Recurrent Neural Networks for Social Media Prediction,” in Proceedings of the 28th ACM International Conference on Multimedia, 2020, pp. 4585-4589.
• -C. Hsu, W.-H. Tseng, and H.-T. Yang, “Learning to Predict Risky Driving Behaviors for Autonomous Driving,” in 2020 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-Taiwan), 2020: IEEE, pp. 1-2.
• -W. Kang, C.-C. Hsu, I.-S. Wang, T.-L. Liu, S.-Y. Chen, and C.-Y. Chang, “Vehicle Trajectory Prediction based on Social Generative Adversarial Network for Self-Driving Car Applications,” in 2020 International Symposium on Computer, Consumer and Control (IS3C), 2020: IEEE, pp. 489-492.
• -X. Zhuang and C.-C. Hsu, “Detecting Generated Image Based on a Coupled Network with Two-Step Pairwise Learning,” in 2019 IEEE International Conference on Image Processing (ICIP), 2019: IEEE, pp. 3212-3216. (Best Student Papwe Award)
• -C. Hsu, L.-W. Kang, C.-Y. Lee, J.-Y. Lee, Z.-X. Zhang, and S.-M. Wu, “Popularity prediction of social media based on multi-modal feature mining,” in Proceedings of the 27th ACM International Conference on Multimedia, 2019, pp. 2687-2691.
• Lugmayr et al., “Aim 2019 challenge on real-world image super-resolution: Methods and results,” in 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019: IEEE, pp. 3575-3583.
• -C. Hsu, H.-T. Ma, and J.-Y. Lee, “SSSNet: Small-Scale-Aware Siamese Network for Gastric Cancer Detection,” in 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2019: IEEE, pp. 1-5.
• -C. Hsu, C.-H. Hung, C.-Y. Jian, and Y.-X. Zhuang, “Stronger Baseline for Vehicle Re-Identification in the Wild,” in 2019 IEEE Visual Communications and Image Processing (VCIP), 2019: IEEE, pp. 1-4.
• -C. Hsu and C.-H. Lin, “Dual Reconstruction with Densely Connected Residual Network for Single Image Super-Resolution,” in IEEE International Conference on Computer Vision Workshop (ICCVW), 2019.
• -T. Su, C.-C. Hsu, Z. Huang, C.-W. Lin, and G. Cheung, “Joint pairwise learning and image clustering based on a siamese cnn,” in 2018 25th IEEE International Conference on Image Processing (ICIP), 2018: IEEE, pp. 1992-1996.
• -C. Hsu et al., “An iterative refinement approach for social media headline prediction,” in Proceedings of the 26th ACM international conference on Multimedia, 2018, pp. 2008-2012.
• -C. Hsu, “Iteration-Free Fractal Mating Coding for Mutual Image Compression,” in 2018 International Symposium on Computer, Consumer and Control (IS3C), 2018: IEEE, pp. 396-399.
• -C. Hsu, C.-Y. Lee, and Y.-X. Zhuang, “Learning to detect fake face images in the wild,” in 2018 International Symposium on Computer, Consumer and Control (IS3C), 2018: IEEE, pp. 388-391.
• -C. Hsu et al., “Social media prediction based on residual learning and random forest,” in Proceedings of the 25th ACM international conference on Multimedia, 2017, pp. 1865-1870. (Oral & Best Grand Challenge Paper Award)
• -C. Hsu and C.-W. Lin, “Objective quality assessment for video retargeting based on spatio-temporal distortion analysis,” in 2017 IEEE Visual Communications and Image Processing (VCIP), 2017: IEEE, pp. 1-4.
• -C. Hsu and C.-W. Lin, “Unsupervised convolutional neural networks for large-scale image clustering,” in 2017 IEEE International Conference on Image Processing (ICIP), 2017: IEEE, pp. 390-394.
• -T. Su, C.-C. Hsu, C.-W. Lin, and W. Lin, “Supervised-learning based face hallucination for enhancing face recognition,” in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016: IEEE, pp. 1751-1755.
• -C. Hsu, L.-W. Kang, and C.-W. Lin, “Video super-resolution via dynamic texture synthesis,” in 2014 IEEE 16th International Workshop on Multimedia Signal Processing (MMSP), 2014: IEEE, pp. 1-6.
• -W. Kang, B. Zhuang, C.-C. Hsu, C.-W. Lin, and C.-H. Yeh, “Self-learning-based low-quality single image super-resolution,” in IEEE Workshop Multimedia Signal Processing (MMSP), Sardinia, Italy, Sept. 2013, 2013. (Top 10% Paper Award)
• -W. Kang, B.-C. Chuang, C.-C. Hsu, C.-W. Lin, and C.-H. Yeh, “Self-learning-based single image super-resolution of a highly compressed image,” in 2013 IEEE 15th International Workshop on Multimedia Signal Processing (MMSP), 2013: IEEE, pp. 224-229.
• -C. Hsu, C.-W. Lin, Y. Fang, and W. Lin, “Objective quality assessment for image retargeting based on perceptual distortion and information loss,” in 2013 Visual Communications and Image Processing (VCIP), 2013: IEEE, pp. 1-6.
• -C. Hsu and C.-W. Lin, “Image super-resolution via feature-based affine transform,” in 2011 IEEE 13th International Workshop on Multimedia Signal Processing, 2011: IEEE, pp. 1-5.
• -Y. Lin, C.-C. Hsu, C.-W. Lin, and L.-W. Kang, “Fast deconvolution-based image super-resolution using gradient prior,” in 2011 Visual Communications and Image Processing (VCIP), 2011: IEEE, pp. 1-4.
• -C. Hsu, C.-W. Lin, C.-T. Hsu, H.-Y. M. Liao, and J.-Y. Yu, “Face hallucination using Bayesian global estimation and local basis selection,” in 2010 IEEE International Workshop on Multimedia Signal Processing, 2010: IEEE, pp. 449-453.
• -C. Hsu, C.-W. Lin, C.-T. Hsu, and H.-Y. M. Liao, “Cooperative face hallucination using multiple references,” in 2009 IEEE International Conference on Multimedia and Expo, 2009: IEEE, pp. 818-821.
• -C. Hsu and H. T. Chang, “Accelerating vector quantization of images using modified run length coding for adaptive block representation and difference measurement,” in 2008 IEEE International Symposium on Circuits and Systems, 2008: IEEE, pp. 3494-3497.
• -C. Hsu, T.-Y. Hung, C.-W. Lin, and C.-T. Hsu, “Video forgery detection using correlation of noise residue,” in 2008 IEEE 10th workshop on multimedia signal processing, 2008: IEEE, pp. 170-174.
• -C. Hsu, H. T. Chang, and T.-C. Chang, “An improved face detection method in low-resolution video,” in Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2007), 2007, vol. 2: IEEE, pp. 419-422.
• -C. Hsu, H. T. Chang, and T.-c. Chang, “Efficient moving object extraction in compressed low-bit-rate video,” in 2006 International Conference on Intelligent Information Hiding and Multimedia, 2006: IEEE, pp. 411-414.

Patents

• Hsu and T.C. Lee, “System and method for diagnosing gastrointestinal neoplasm,” US Patent, US11011275B2, 5/18/2021.
• 童曉儒, 許志仲, 張文誠, “影像辨識方法”, Taiwan Patent, #I689872, 4/1 /2020.
• C. Hsu and C.W. Lin, “Method and system for example-based face hallucination,” USA patent # 8,488,913, 2013.
• C. Hsu and H.S. Chang, “影像辨識系統及其操作方法,” Taiwan Patent #201329874, 2013.
• C. Hsu, and C.W. Lin, “Super-resolution method and system for human face based on sample,” China patent #CN102298775 B, 2013.
• 張廷政, 張軒庭, 與許志仲, “The application of parallel-connected distributed encryption system in internet,” Taiwan Patent #094122766, 2005.
• 許志仲, 王焜潔, 與張廷政, “應用於網際網路上之分散式加密方法,” Taiwan Patent #093135605, 2004.