{"id":1632,"date":"2026-03-30T00:24:37","date_gmt":"2026-03-29T16:24:37","guid":{"rendered":"https:\/\/cchsu.info\/wordpress\/zh\/publications\/"},"modified":"2026-04-01T18:13:51","modified_gmt":"2026-04-01T10:13:51","slug":"publications","status":"publish","type":"page","link":"https:\/\/cchsu.info\/wordpress\/zh\/publications\/","title":{"rendered":"\u8ad6\u6587\u8457\u4f5c"},"content":{"rendered":"

\u8a9e\u8a00\uff1a<\/strong>English<\/a> | \u7e41\u9ad4\u4e2d\u6587<\/p>\n

\u7814\u7a76\u7bc4\u570d\u3002<\/strong> ACVLab \u7684\u8ad6\u6587\u8207\u6280\u8853\u6210\u679c\u5927\u81f4\u53ef\u6b78\u7d0d\u70ba\u56db\u500b\u5f7c\u6b64\u9023\u52d5\u7684 pillars\uff1aAssured Visual Intelligence<\/b>\uff08DeepFake detection\u3001adversarial defense\u3001proactive media authentication\uff09\u3001Lean Visual Architectures<\/b>\uff08prefix-scan attention\u3001bitstream-level inference\u3001adaptive quantization\uff09\u3001Autonomous Visual Perception<\/b>\uff083D reconstruction\u3001self-driving\u3001physics-aligned scene recovery\uff09\uff0c\u4ee5\u53ca Broad-Spectrum Scientific Sensing<\/b>\uff08hyperspectral restoration\u3001satellite compressed sensing\u3001cross-spectral forensics\uff09\u3002<\/p>\n

\u5b8c\u6574\u8ad6\u6587\u5217\u8868<\/a><\/span><\/strong><\/h4>\n

\u7cbe\u9078\u671f\u520a\u8ad6\u6587\uff08* \u901a\u8a0a\u4f5c\u8005\uff09<\/h2>\n

(JCR Impact Factors and Rankings: JCR 2025 (JIF 2024), released June 2025; <\/em>CiteScore and Rankings: Scopus CiteScore 2024, released June 2025)<\/em><\/p>\n

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  1. C.-H. Cheng, C.-M. Lee, W.-T. Liao, Y.-F. Lin, Y.-C. Cheng, F.-E. Yang, Y.-C. F. Wang, and C.-C. Hsu*<\/strong>, \u201cPromptHSI: Universal Hyperspectral Image Restoration with Vision-Language Modulated Frequency Adaptation,\u201d IEEE Transactions on Geoscience and Remote Sensing (TGRS)<\/em>, Early Access, Feb. 2026. [IEEE Xplore<\/a>] [DOI<\/a>] [arXiv<\/a>] [GitHub<\/a>] (IF: 8.6; CiteScore: 13.6)<\/strong><\/li>\n
  2. C.-Y. Lai, C.-Y. Jian, P.-C. Chuang, C.-M. Lee, C.-C. Hsu*, C.-T. Hsu, and C.-W. Lin, \u201cUMCL: Unimodal-Generated Multimodal Contrastive Learning for Cross-compression-rate Deepfake Detection,\u201d International Journal of Computer Vision (IJCV)<\/em>, 2026. [Springer<\/a>] [DOI<\/a>] (IF: 9.3; CiteScore: 25.9)<\/strong><\/li>\n
  3. C.-C. Hsu*<\/strong>, S.-N. Chen, M.-H. Wu, C.-M. Lee, Y.-F. Wang, and Y.-S. Chou, \u201cTowards Robust DeepFake Detection under Unstable Face Sequences: Adaptive Sparse Graph Embedding with Order-Free Representation and Explicit Laplacian Spectral Prior,\u201d submitted to IEEE Transactions on Information Forensics and Security (TIFS)<\/em>, 2026. [arXiv<\/a>]<\/li>\n
  4. C.-C. Hsu*, C.-Y. Jian, E.-S. Tu, C.-M. Lee, and G.-L. Chen, \u201cReal-Time Compressed Sensing for Joint Hyperspectral Image Transmission and Restoration for CubeSat,\u201d IEEE Transactions on Geoscience and Remote Sensing<\/em>, Apr. 2024. \u00a0(IF: <\/strong>8.6, Rank 4\/101=4% in Geochemistry & Geophysics<\/strong>; CiteScore: <\/strong>13.6, Rank 7\/198=4% in Earth and Planetary Sciences (all)<\/strong>)<\/strong><\/li>\n
  5. C.-H. Lin, C.-C. Hsu*, S.-S. Young, C.-Y. Hsieh, and S.-C. Tai, \u201cQRCODE: Quasi-residual Convex Deep Network for Fusing Misaligned Hyperspectral and Multispectral Images,\u201d IEEE Transactions on Geoscience and Remote Sensing<\/em>, Apr. 2024. \u00a0(IF: <\/strong>8.6, Rank 4\/101=4% in Geochemistry & Geophysics<\/strong>; CiteScore: <\/strong>13.6, Rank 7\/198=4% in Earth and Planetary Sciences (all)<\/strong>)<\/strong><\/li>\n
  6. G.-L. Chen and C.-C. Hsu*, \u201cJointly Defending DeepFake Manipulation and Adversarial Attack using Decoy Mechanism,\u201d IEEE Transactions on Pattern Analysis and Machine Intelligence<\/em>, Aug. 2023. \u00a0(IF: <\/strong>18.6, Rank 3\/368=1% in Engineering, Electrical & Electronic<\/strong>; CiteScore: <\/strong>35.0, Rank 1\/665=<1% in Mathematics, Applied Mathematics<\/strong>)<\/strong><\/li>\n
  7. C.-H. Lin, Y. Liu, C.-Y. Chi, C.-C. Hsu, H. Ren, and T.-Q. Quek, \u201cHyperspectral Tensor Completion Using Low-Rank Modeling and Convex Functional Analysis,\u201d IEEE Transactions on Neural Networks and Learning Systems<\/em>, Aug. 2024. \u00a0(IF: <\/strong>8.9, Rank 18\/368=5% in Engineering, Electrical & Electronic<\/strong>; CiteScore: <\/strong>24.7, Rank 5\/507=1% in Computer Networks and Communications<\/strong>)<\/strong><\/li>\n
  8. F. Bougourzi, C. Distante, F. Dornaika, A. Taleb-Ahmed, A. Hadid, S. Chaudhary, W. Yang, Y. Qiang, T. Anwar, M. E. Breaban, C.-C. Hsu, S.-C. Tai, S.-N. Chen, et al., \u201cCOVID-19 Infection Percentage Estimation from Computed Tomography Scans: Results and Insights from the International Per-COVID-19 Challenge,\u201d Sensors<\/em>, vol. 24, no. 5, Art. 1557, 2024. [DOI<\/a>]<\/li>\n
  9. J.-Y. Yang, T.-C. Lee, W.-T. Liao, C.-C. Hsu*, \u201cMulti-Head Self-Attention Mechanism Enabled Individualized Hemoglobin Prediction and Treatment Recommendation Systems in Anemia Management for Hemodialysis Patients,\u201d Heliyon<\/em>, Feb. 2023. \u00a0(IF: <\/strong>3.6, Rank 16\/73=22% Q1 in Multidisciplinary Sciences<\/strong>; CiteScore: <\/strong>4.1, Rank 39\/200=20% Q1 in Multidisciplinary<\/strong>)<\/strong><\/li>\n
  10. C.-Y. Wei, W.-Y. Huang, C.-Y. Jian, C.-C. H. Hsu, C.-C. Hsu, et al., \u201cSemantic Segmentation Guided Detector for Segmentation, Classification, and Lesion Mapping of Acute Ischemic Stroke in MRI Images,\u201d NeuroImage: Clinical<\/em>, Mar. 2023. \u00a0(IF: <\/strong>3.6, Rank 4\/16=25% Q1 in Neuroimaging<\/strong>; CiteScore: <\/strong>7.8, Rank 37\/346=11% in Radiology, Nuclear Medicine and Imaging<\/strong>)<\/strong><\/li>\n
  11. 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., \u201cADAM Challenge: Detecting Age-related Macular Degeneration from Fundus Images,\u201d IEEE Transactions on Medical Imaging<\/em>, doi: 10.1109\/TMI.2022.3172773, May 2022. (IF: <\/strong>9.8, Rank 2\/85=2% in Radiology, Nuclear Medicine & Medical Imaging<\/strong>; CiteScore: <\/strong>18.3, Rank 28\/970=3% in Electrical and Electronic Engineering<\/strong>)<\/strong><\/li>\n
  12. C.-C. Hsu, C.-H. Lin, C.-H. Kao, and Y.-C. Lin, \u201cDCSN: Deep Compressed Sensing Network for Efficient Hyperspectral Data Transmission of Miniaturized Satellite,\u201d IEEE Transactions on Geoscience and Remote Sensing<\/em>, vol. 59, no. 9, pp. 7773-7789, Sept. 2021. \u00a0(IF: <\/strong>8.6, Rank 4\/101=4% in Geochemistry & Geophysics<\/strong>; CiteScore: <\/strong>13.6, Rank 7\/198=4% in Earth and Planetary Sciences (all)<\/strong>)<\/strong><\/li>\n
  13. C.-C. Hsu, Y.-X. Zhuang, and C.-Y. Lee, \u201cDeep Fake Image Detection Based on Pairwise Learning,\u201d Applied Sciences-Basel<\/em>, vol. 10, no. 1, p. 370, Jan. 2020. \u00a0(IF: <\/strong>2.5, Rank 46\/161=29% Q2 in Physics, Applied<\/strong>; CiteScore: <\/strong>5.5, Rank 72\/344=21% Q1 in General Engineering<\/strong>)<\/strong><\/li>\n
  14. C.-C. Hsu, C.-W. Lin, W.-T. Su, and G. Cheung, \u201cSiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination,\u201d IEEE Transactions on Image Processing<\/em>, vol. 28, no. 12, pp. 6225-6236, Dec. 2019. \u00a0(IF: <\/strong>13.7, Rank 9\/368=2% in Engineering, Electrical & Electronic<\/strong>; CiteScore: <\/strong>22.5, Rank 3\/127=2% in Computer Graphics and Computer-Aided Design<\/strong>)<\/strong><\/li>\n
  15. C.-C. Hsu and C.-W. Lin, \u201cCNN-based Joint Clustering and Representation Learning with Feature Drift Compensation for Large-Scale Image Data,\u201d IEEE Transactions on Multimedia<\/em>, vol. 20, no. 2, pp. 421-429, 2017. \u00a0(IF: <\/strong>9.7, Rank 4\/130=3% in Telecommunications<\/strong>; CiteScore: <\/strong>12.9, Rank 3\/69=4% in Media Technology<\/strong>)<\/strong><\/li>\n
  16. C.-C. Hsu, L. Kang, and C.-W. Lin, \u201cTemporally Coherent Super-Resolution of Textured Video via Dynamic Texture Synthesis,\u201d IEEE Transactions on Image Processing<\/em>, vol. 24, no. 3, pp. 919-931, 2015. \u00a0(IF: <\/strong>13.7, Rank 9\/368=2% in Engineering, Electrical & Electronic<\/strong>; CiteScore: <\/strong>22.5, Rank 3\/127=2% in Computer Graphics and Computer-Aided Design<\/strong>)<\/strong><\/li>\n
  17. Kang, C.-C. Hsu, B.-Q. Zhuang, C.-W. Lin, and C.-H. Yeh, \u201cLearning-Based Joint Super-Resolution and Deblocking for a Highly Compressed Image,\u201d IEEE Transactions on Multimedia<\/em>, vol. 17, no. 7, pp. 921-934, Jul. 2015. (IF: <\/strong>9.7, Rank 4\/130=3% in Telecommunications<\/strong>; CiteScore: <\/strong>12.9, Rank 3\/69=4% in Media Technology<\/strong>)<\/strong><\/li>\n
  18. C.-C. Hsu, C.-W. Lin, Y.-M. Fang, and W.-S. Lin, \u201cObjective Quality Assessment for Image Retargeting Based on Perceptual Geometric Distortion and Information Loss,\u201d IEEE Journal of Selected Topics in Signal Processing<\/em>, vol. 8, no. 3, pp. 377-389, Jun. 2014. \u00a0(IF: <\/strong>13.7, Rank 8\/368=2% in Engineering, Electrical & Electronic<\/strong>; CiteScore: <\/strong>24.3, Rank 2\/183=1% in Signal Processing<\/strong>)<\/strong><\/li>\n<\/ol>\n

    \u66f8\u7c4d\u7ae0\u7bc0<\/h2>\n
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    1. H.T. Chang<\/a> and C.C. Hsu<\/strong>, “Fractal-based Secured multiple-image distribution and compression,” in Handbook of Research on Secure Multimedia Distribution<\/em>, Chapter 27, Edited by Dr. Shiguo Lian and Dr. Yan Zhang, ISBN: 978-1-60566-262-6, 2008.<\/li>\n<\/ol>\n

      \u7cbe\u9078\u6703\u8b70\u8ad6\u6587\u00a0<\/h2>\n
        \n
      1. C.-M. Lee, Y.-F. Lin, Y.-J. Hsiao, J.-H. Jiang, Y.-L. Liu, and C.-C. Hsu<\/strong>, \u201cPhaSR: Generalized Image Shadow Removal with Physically Aligned Priors,\u201d accepted to IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)<\/em>, 2026. [arXiv<\/a>] [GitHub<\/a>]<\/li>\n
      2. C.-M. Lee, Y.-F. Lin, J.-H. Jiang, Y.-J. Hsiao, C.-C. Hsu<\/strong>, and Y.-L. Liu, \u201cReflexSplit: Single Image Reflection Separation via Layer Fusion-Separation,\u201d accepted to IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)<\/em>, 2026. [arXiv<\/a>] [GitHub<\/a>]<\/li>\n
      3. C.-C. Hsu<\/strong>, X.-D. Ma, W.-T. Liao, and C.-M. Lee, \u201cELSA: Exact Linear-Scan Attention for Fast and Memory-Light Vision Transformers,\u201d accepted to the CVPR 2026 Findings Workshop<\/em>, 2026.<\/li>\n
      4. J.M. Chen, Y.H. Chao, Y.J. Wang, M.D. Shieh, C.C. Hsu<\/strong>, and W.F. Lin, \u201cQuantTune: Optimizing Model Quantization with Adaptive Outlier-Driven Fine Tuning,\u201d in 2025 IEEE 8th International Conference on Multimedia Information Processing and Retrieval (MIPR)<\/em>, San Jose, CA, USA, 2025, pp. 130-136. [arXiv<\/a>] [IEEE Xplore<\/a>]<\/li>\n
      5. H. Cheng, J. W. Lee, C. M. Lee, and\u00a0C. C. Hsu<\/span><\/strong>, “WWE-UIE: A Wavelet & White Balance Efficient Network for Underwater Image Enhancement,” in\u00a0<\/span>2026 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV)<\/span><\/em>, 2026.<\/span><\/li>\n
      6. H. Cheng and C.C. Hsu<\/strong>.<\/em>, “ChangeDINO: DINOv3-Driven Building Change Detection in Optical Remote Sensing Imagery,”\u00a0ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (XXV ISPRS Congress 2026)<\/em>, 2026.<\/li>\n
      7. -M. Lee, Y.-H. Ho, Y.-F. Lin, J.-W. Lee, L.-W. Kang, and C.-C. Hsu<\/strong>, “HSSDCT: Factorized Spatial-Spectral Correlation for Hyperspectral Image Fusion,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP)<\/em>, Barcelona, Spain, May 2026.<\/li>\n
      8. Y.F. Lin, C.M. Lee, C.C. Hsu*<\/strong>, “DenseSR: Image Shadow Removal as Dense Prediction,” in Proceedings of ACM Multimedia<\/em>, Ireland, Oct. 2025.<\/li>\n
      9. \n
        C.M. Lee, B.C. Qiu, C.J. Kang, Y.H. Wu, J.L. Chen, Y.F. Lin, Y.S. Chou, C.C. Hsu*<\/strong>, “Anchoring Trends: Mitigating Social Media Popularity Prediction Drift via Feature Clustering and Expansion,”\u00a0<\/span>in <\/span>Proceedings of ACM Multimedia<\/em>, Ireland, Oct. 2025.<\/span><\/div>\n<\/li>\n
      10. R.L. Jian, M.C. Luo, C.W. Huang, C.M. Lee, Y.F. Lin, C.C. Hsu*<\/strong>, “GTATrack: Winner Solution to SoccerTrack 2025 with Deep-EIoU and Global Tracklet Association,” in <\/span>Proceedings of ACM Multimedia<\/em>, Ireland, Oct. 2025.<\/span><\/li>\n
      11. C.M. Lee, B.C. Qiu, T.Y. Chen, M.H. Sun, F.Y. Lin, J.T. Tsai, I-A. Tsai, Y.F. Lin, C.C. Hsu*<\/strong>, “Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization,” in Proceedings of IEEE International Conference on Computer Vision Workshop<\/em>, Hawaii, Nov. 2025.<\/li>\n\n
      12. C.-M. Lee, Y.-F. Lin, L.-W.\u00a0<\/span>Kang,\u00a0<\/span>C.-C. Hsu*<\/span><\/strong>, “Robust Hyperspectral Image Pansharpening via Sparse Spatial-Spectral Representation,”\u00a0<\/span>IEEE International Geoscience and Remote Sensing Symposium (IGARSS)<\/span><\/em>, Brisbane, Australia, 3-8 Aug. 2025.<\/span><\/li>\n
      13. C.-C. Hsu*<\/strong>, C.-M. Lee, M.-Z. Ko, E.-Z. Liu, Y.-C. Cheng, M.-C. Chang, “HyperForensics++: Toward Adversarial Perturbed and Object Replacement in Hyperspectral Imaging Dataset,” IEEE International Geoscience and Remote Sensing Symposium (IGARSS)<\/em>, Brisbane, Australia, 3-8 Aug. 2025..<\/span><\/li>\n
      14. C.-M. Lee, Y.-F. Lin, Y.-H. Ho, L.-W. Kang, C.-C. Hsu*<\/strong>, “HyFusion: Enhanced Reception Field Transformer for Hyperspectral Image Fusion,” IEEE International Geoscience and Remote Sensing Symposium (IGARSS)<\/em>, Brisbane, Australia, 3-8 Aug. 2025..<\/span><\/li>\n
      15. C.C. Hsu<\/strong>, C.M. Lee, P.T. Yu, C.J. Kang, C.H. Sun, and K.M. Wu, “Progressive Alignment with VLM-LLM Feature to Augment Defect Classification for the ASE Dataset,” in Proc. IEEE Int. Conf. Consumer Electronics (ICCE)<\/em>, Las Vegas, NV, USA, Jan. 4-6, 2025.<\/li>\n
      16. C.C. Hsu<\/strong>, C.-M. Lee, P.-T. Yu, C.-H. Sun, K.-M. Wu, “OCR is All you need: Importing Multi-Modality into Image-based Defect Classification System,” in Proc. IEEE Asia Pacific Conf. Circuits and Systems (APCCAS)<\/em>, Taipei, Taiwan, Nov. 7-9, 2024.<\/li>\n
      17. C.Y. Lai, C.T. Hsu, C.W. Lin, and C.C. Hsu<\/strong>, “Prompt-guided Multi-modal contrastive learning for Cross-compression-rate Deepfake Detection,” in Proc. British Machine Vision Conf. (BMVC)<\/em>, Glasgow, UK, Nov. 25-28, 2024.<\/li>\n
      18. C.C. Hsu<\/strong>, C.M. Lee, Y.F. Lin, Y.S. Chou, C.Y. Jian, C.H. Tsai, “Revisiting Vision-Language Features Adaptation and Inconsistency for Social Media Popularity Prediction,” in Proc. 32nd ACM Int. Conf. Multimedia (ACM MM)<\/em>, Melbourne, Australia, Oct. 28 – Nov. 1, 2024.<\/li>\n
      19. C.C. Hsu<\/strong>, Y.Z. Jiang, W.H. Huang, “VCDSET: A New Vehicle Collision Dataset in Asia Countries for Anticipating Accidents,” in Proc. IEEE Int. Conf. Image Processing (ICIP)<\/em>, Abu Dhabi, UAE, Oct. 27-30, 2024.<\/li>\n
      20. C.C. Hsu<\/strong>, M.H. Wu, E.C. Liu, “LFGN: Low-Level Feature-Guided Network for Adversarial Defense,” in Proc. IEEE Int. Conf. Image Processing (ICIP)<\/em>, Abu Dhabi, UAE, Oct. 27-30, 2024.<\/li>\n
      21. C.C. Hsu<\/strong>, C.M. Lee, and Y.S. Chou, “DRCT: Saving Image Super-Resolution away from Information Bottleneck,” in Proc. New Trends in Image Restoration and Enhancement (NTIRE) Workshop at IEEE\/CVF Conf. Computer Vision and Pattern Recognition (CVPR)<\/em>, Seattle, WA, USA, June 17-21, 2024.<\/li>\n
      22. C.C. Hsu<\/strong>, C.M. Lee, Y.F. Chiang, Y.S. Chou, C.Y. Jiang, S.C. Tai, and C.H. Tsai, “A Closer Look at Spatial-Slice Features for COVID-19 Detection,” in Proc. Domain adaptation, Explainability, Fairness in AI for Medical Image Analysis (DEF-AI-MIA) Workshop at IEEE\/CVF Conf. Computer Vision and Pattern Recognition (CVPR)<\/em>, Seattle, WA, USA, June 17-21, 2024.<\/li>\n
      23. C.C. Hsu<\/strong> et al., “OmniDet: Omnidirectional Object Detection via Fisheye Camera Adaptation,” in Proc. 7th IEEE Int. Conf. Multimedia Information Processing and Retrieval (MIPR)<\/em>, San Jose, CA, USA, Aug. 7-9, 2024.<\/li>\n
      24. C.C. Hsu<\/strong> et al., “MISS: Memory-efficient Instance Segmentation For Sport-Scenes With Visual Inductive Priors,” in Proc. 7th IEEE Int. Conf. Multimedia Information Processing and Retrieval (MIPR)<\/em>, San Jose, CA, USA, Aug. 7-9, 2024.<\/li>\n
      25. C.C. Hsu<\/strong>, et al., “Adapting Object Detection to Fisheye Cameras: A Knowledge Distillation with Semi-Pseudo-Label Approach,” in Proceedings of ACM Multimedia Asia<\/em>, Tainan Taiwan, Dec. 2023.<\/li>\n
      26. C.C. Hsu<\/strong>, C.M. Lee, X.Y. Hou, and C.H. Tsai, “Gradient Boost Tree Network based on Extensive Feature Analysis for Popularity Prediction of Social Posts,” in Proceedings of ACM Multimedia<\/em>, Ottawa, Canada, Nov. 2023.<\/li>\n
      27. JY Yang, TC Lee, CY Jian, C.C. Hsu*<\/strong>, “Multimodality-aware Intra-dialytic Hypotension Classifier: A Bilingual NLP Approach to Classify Dialysis Records,” in Proceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics<\/em>, USA, Oct. 2023<\/li>\n
      28. C. C. Hsu<\/strong>, 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<\/em>, Brisbane, Australia, 10-14 July 2023.<\/li>\n
      29. C. C. Hsu,<\/strong> 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)<\/em>, June 2023.<\/li>\n
      30. C.C. Hsu<\/strong> and M.Z. Ke, “Seeing is NOT Believing: Toward Forgery Detection for Hyperspectral Image,” IEEE International Geoscience and Remote Sensing Symposium (IGARSS)<\/em>, July 2023.<\/li>\n
      31. C.C. Hsu<\/strong>, 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<\/em>, 23-27 Oct. 2022.<\/li>\n
      32. C.C. Hsu<\/strong>, 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, <\/em>10-14 Oct. 2022.<\/li>\n
      33. C.C. Hsu<\/strong>, 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)<\/em>, Taipei City, Taiwan, 2022 pp. 1-6.<\/li>\n
      34. B.S. Huang, C.C. Hsu<\/strong>, 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<\/em> (ICASSP)<\/em>, 22-27 May 2022.<\/li>\n
      35. G.L. Chen, C.C. Hsu<\/strong>, and M.H. Wu, \u201cAdaptive Distribution Learning with Statistical Hypothesis Testing for COVID-19 CT Scan Classification,\u201d in Proc. of IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW), Oct. 2021. <\/em><\/li>\n
      36. C.C. Hsu, <\/strong>C, Lee, C. Lin, K.M. Hung, Y.L. Lin, X.Y. Wang, \u201cEfficient-ROD: Efficient Radar Object Detection based on Densely Connected Residual Network,\u201d in Proc. of ACM International Conference on Multimedia Retrieval (ICMR)<\/em>, 21-24 Aug., 2021.<\/li>\n
      37. C.-C. Hsu<\/strong>, 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<\/em>, 2020, pp. 4585-4589.<\/li>\n
      38. C.-C. Hsu<\/strong>, 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)<\/em>, 2020: IEEE, pp. 1-2.<\/li>\n
      39. Y.-X. Zhuang and C.-C. Hsu<\/strong>, “Detecting Generated Image Based on a Coupled Network with Two-Step Pairwise Learning,” in 2019 IEEE International Conference on Image Processing (ICIP)<\/em>, 2019: IEEE, pp. 3212-3216. (Best Student Paper Award)<\/span><\/li>\n
      40. C.-C. Hsu<\/strong>, 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<\/em>, 2019, pp. 2687-2691.<\/li>\n
      41. Lugmayr et al.<\/em>, “Aim 2019 challenge on real-world image super-resolution: Methods and results,” in 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW)<\/em>, 2019: IEEE, pp. 3575-3583.<\/li>\n
      42. C.-C. Hsu<\/strong>, 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)<\/em>, 2019: IEEE, pp. 1-4.<\/li>\n
      43. C.-C. Hsu<\/strong> 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)<\/em>, 2019.<\/li>\n
      44. W.-T. Su, C.-C. Hsu<\/strong>, 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)<\/em>, 2018: IEEE, pp. 1992-1996.<\/li>\n
      45. C.-C. Hsu<\/strong> et al.<\/em>, “An iterative refinement approach for social media headline prediction,” in Proceedings of the 26th ACM international conference on Multimedia<\/em>, 2018, pp. 2008-2012.<\/li>\n
      46. C.-C. Hsu<\/strong>, 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)<\/em>, 2018: IEEE, pp. 388-391.<\/li>\n
      47. C.-C. Hsu<\/strong> et al.<\/em>, “Social media prediction based on residual learning and random forest,” in Proceedings of the 25th ACM international conference on Multimedia<\/em>, 2017, pp. 1865-1870. (Oral & Best Grand Challenge Paper Award)<\/span><\/li>\n
      48. C.-C. Hsu<\/strong> 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)<\/em>, 2017: IEEE, pp. 1-4.<\/li>\n
      49. C.-C. Hsu<\/strong> and C.-W. Lin, “Unsupervised convolutional neural networks for large-scale image clustering,” in 2017 IEEE International Conference on Image Processing (ICIP)<\/em>, 2017: IEEE, pp. 390-394.<\/li>\n
      50. W.-T. Su, C.-C. Hsu<\/strong>, 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)<\/em>, 2016: IEEE, pp. 1751-1755.<\/li>\n
      51. C.-C. Hsu<\/strong>, 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)<\/em>, 2014: IEEE, pp. 1-6.<\/li>\n
      52. L.-W. Kang, B. Zhuang, C.-C. Hsu,<\/strong> C.-W. Lin, and C.-H. Yeh, “Self-learning-based low-quality single image super-resolution,” in IEEE Workshop Multimedia Signal Processing\u00a0<\/em>(MMSP<\/em>), Sardinia, Italy, Sept. 2013, 2013. (Top 10% Paper Award)<\/li>\n
      53. C.-C. Hsu<\/strong>, 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)<\/em>, 2013: IEEE, pp. 1-6.<\/li>\n
      54. C.-C. Hsu<\/strong> and C.-W. Lin, “Image super-resolution via feature-based affine transform,” in 2011 IEEE 13th International Workshop on Multimedia Signal Processing<\/em>, 2011: IEEE, pp. 1-5.<\/li>\n
      55. C.-C. Hsu<\/strong>, 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<\/em>, 2010: IEEE, pp. 449-453.<\/li>\n
      56. C.-C. Hsu<\/strong>, 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<\/em>, 2008: IEEE, pp. 170-174.<\/li>\n<\/ol>\n

        \u5c08\u5229<\/strong><\/h2>\n
          \n
        1. C.-C. Hsu<\/strong>, C.-Y. Chien, Y.-S. Tu, C.-M. Lee, and K.-L. Chen, “COMPRESSION SENSING METHOD FOR REMOTE SENSING IMAGES, REMOTE SENSING DEVICE AND IMAGE RECONSTRUCTION DEVICE,” Taiwan Patent I 902310, Oct. 21, 2025.<\/li>\n
        2. C. Hsu <\/strong>and T.C. Lee, \u201cSystem and method for diagnosing gastrointestinal neoplasm,\u201d US Patent, US11011275B2, 5\/18\/2021.<\/li>\n
        3. \u7ae5\u66c9\u5112, \u8a31\u5fd7\u4ef2, \u5f35\u6587\u8aa0, “\u5f71\u50cf\u8fa8\u8b58\u65b9\u6cd5”, Taiwan Patent, #I689872<\/a>, 4\/1 \/2020.<\/li>\n
        4. \u8a31\u5fd7\u4ef2\u8207\u5f35\u8ed2\u97f6\uff0c”\u5f71\u50cf\u8fa8\u8b58\u7cfb\u7d71\u53ca\u5176\u64cd\u4f5c\u65b9\u6cd5,” Taiwan Patent #201329874, 2013.<\/li>\n
        5. \u8a31\u5fd7\u4ef2\u8207\u6797\u5609\u6587\uff0c”\u5229\u7528\u4eff\u5c04\u8f49\u63db\u5efa\u7acb\u5f71\u50cf\u8cc7\u6599\u5eab\u4e4b\u65b9\u6cd5,” Taiwan Patent #201317938, 2013.<\/li>\n
        6. Chih-Chung Hsu and Chia-Wen Lin, \u201cMethod and system for example-based face hallucination,\u201d USA patent # 8,488,913, 2013.<\/li>\n
        7. Chih-Chung Hsu and Chia-Wen Lin, “Super-resolution method and system for human face based on sample,” China patent #CN102298775 B, 2013.<\/li>\n
        8. \u6797\u5609\u6587\u8207\u8a31\u5fd7\u4ef2\uff0c”\u4ee5\u6a23\u672c\u70ba\u57fa\u790e\u4e4b\u4eba\u81c9\u8d85\u89e3\u6790\u5ea6\u65b9\u6cd5\u8207\u7cfb\u7d71,” Taiwan Patent #201145181, 2011.<\/li>\n
        9. \u5f35\u5ef7\u653f, \u5f35\u8ed2\u5ead, \u8207\u8a31\u5fd7\u4ef2, “\u61c9\u7528\u65bc\u7db2\u969b\u7db2\u8def\u4e0a\u4e4b\u5206\u6563\u5f0f\u4e26\u806f\u52a0\u5bc6\u65b9\u6cd5,” Taiwan Patent #094122766, 2005.<\/li>\n
        10. \u8a31\u5fd7\u4ef2, \u738b\u711c\u6f54, \u8207\u5f35\u5ef7\u653f, “\u61c9\u7528\u65bc\u7db2\u969b\u7db2\u8def\u4e0a\u4e4b\u5206\u6563\u5f0f\u52a0\u5bc6\u65b9\u6cd5,” Taiwan Patent #093135605, 2004.<\/li>\n<\/ol>\n

          \u00a0<\/h2>\n

          \u6295\u7a3f\u4e2d\u8ad6\u6587\uff08* \u901a\u8a0a\u4f5c\u8005\uff09<\/h2>\n
          \n
            \n
          • C.-M. Lee, J.-W. Lee, F.-E. Yang, Y.-C. F. Wang, C.-C. Hsu*<\/strong>, “FracQuant: Fractal Complexity Assessment for Content-aware Image Super-resolution Network Quantization,” submitted to\u00a0ICIP<\/em>, 2026.<\/span><\/li>\n
          • N. Lizarti, J.-C. Chen, C.-C. Hsu, <\/strong>C.-W. Lin, “VisForensics: Exposing Deception with a Robust Attentive Graph Convolution Network and Ensemble Learning for Deepfake Detection<\/em>,” submitted to ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)<\/em>, 2025.<\/span><\/li>\n
          • C.-C. Hsu*<\/strong>, Senior Member, IEEE, C.-M. Lee, C.-C. Ni, Y.-F. Lin, L.-W. Kang, Member, IEEE, “S3<\/sup>RNet: Sparse Spatial-Spectral Representation with Knowledge Distillation for Lightweight Hyperspectral Image Pansharpening<\/em>,” submitted to JSTAR,<\/em> 2025.<\/span><\/li>\n<\/ul>\n<\/div>\n\n\n

            <\/p>\n","protected":false},"excerpt":{"rendered":"

            \u8a9e\u8a00\uff1aEnglish | \u7e41\u9ad4\u4e2d\u6587 \u7814\u7a76\u7bc4\u570d\u3002 ACVLab \u7684\u8ad6\u6587\u8207\u6280\u8853\u6210\u679c\u5927\u81f4\u53ef\u6b78\u7d0d\u70ba\u56db\u500b\u5f7c\u6b64\u9023\u52d5\u7684 pi […]<\/p>\n","protected":false},"author":0,"featured_media":0,"parent":1629,"menu_order":3,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1632","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/cchsu.info\/wordpress\/wp-json\/wp\/v2\/pages\/1632","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cchsu.info\/wordpress\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/cchsu.info\/wordpress\/wp-json\/wp\/v2\/types\/page"}],"replies":[{"embeddable":true,"href":"https:\/\/cchsu.info\/wordpress\/wp-json\/wp\/v2\/comments?post=1632"}],"version-history":[{"count":4,"href":"https:\/\/cchsu.info\/wordpress\/wp-json\/wp\/v2\/pages\/1632\/revisions"}],"predecessor-version":[{"id":1708,"href":"https:\/\/cchsu.info\/wordpress\/wp-json\/wp\/v2\/pages\/1632\/revisions\/1708"}],"up":[{"embeddable":true,"href":"https:\/\/cchsu.info\/wordpress\/wp-json\/wp\/v2\/pages\/1629"}],"wp:attachment":[{"href":"https:\/\/cchsu.info\/wordpress\/wp-json\/wp\/v2\/media?parent=1632"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}