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<!DOCTYPE html>
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<title>IIP · Fei Gao</title>
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<h5 class="mb-0">Fei Gao</h5>
<small>Xianghu Elite Professor</small>
<p class="mb-2"><small> fgao <i class="fa fa-at"></i> xidian.edu.cn </small> </p>
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<h5 class="section-title ff-secondary text-start text-primary fw-normal">About Me</h5>
<h1 class="mb-4">Fei Gao</h1>
<p class="mb-4">Fei Gao is currently with the Hangzhou Institute of Technology, in Xidian University. He received his Bachelor Degree in Electronic Engineering and Ph.D. Degree in Information and Communication Engineering from Xidian University (Xi’an, China) in 2009 and 2015, respectively. From Oct. 2012 to Sep. 2013, he was a Visiting Ph.D. Candidate in University of Technology, Sydney (UTS) in Australia.</p>
<p class="mb-4">He mainly applies machine learning techniques to computer vision problems. His research interests include visual quality assessment and enhancement, intelligent visual arts generation, biomedical image analysis, etc. His research results have expounded in more than 30 publications at prestigious journals and conferences. He serverd for a number of journals and conferences.</p>
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<h6 class="text-uppercase mb-0">Publications</h6>
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<h5 class="mb-0">Yiji Feng</h5>
<small>2024- (Ph.D.)</small>
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<h5 class="mb-0">Yongjie Hu</h5>
<small>2024-</small>
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<h5 class="mb-0">Yue Tang </h5>
<small>2024-</small>
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<h5 class="mb-0">Weichang Wang </h5>
<small>2024-</small>
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<h5 class="mb-0">Jiangpeng Wang </h5>
<small>2024-</small>
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<h5 class="mb-0">Derui Zhang </h5>
<small>2023-</small>
</div>
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<h5 class="mb-0">Wenwang Han </h5>
<small>2023-</small>
</div>
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<h5 class="mb-0">Jiaqi Shi </h5>
<small>2023-</small>
</div>
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<h5 class="mb-0">Yifan Jiang</h5>
<small>2022-</small>
</div>
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<h5 class="mb-0">Si Shi</h5>
<small>2022-</small>
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<span>AI + Art</span>
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<ul>
<li>Artificial Intelligence Generated Content (AIGC)</li>
<li>Low-level Analysis of Art Images, e.g. detection, segmentation</li>
<li>Image Aesthetic Assessment (IAA) and Enhancement</li>
<li>Facial Image Quality Assessment (FIQA)</li>
<li>Generative Adversarial Networks (GAN), Diffusion Model, Cross-modal Learning</li>
</ul>
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<li>Intelligent Early Screening for Breast Cancer</li>
<li>Pulmonary Nodules classification for Lung Cancer</li>
<li>Brain Imaging Analysis and Generation，e.g. MRI, CT</li>
<li>Transformers, Generative Adversarial Networks (GAN), Cross-modal Learning.</li>
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<h1 class="mb-5">Selected Publications </h1>
<p>
<a href="https://dblp.org/pid/16/722-6.html" target="_blank"> [DBLP] </a>
<a href="https://orcid.org/0000-0002-8714-0975" target="_blank"> [ORCID] </a>
<a href="https://scholar.google.com/citations?user=wawnisoAAAAJ" target="_blank"> [Google Scholar] </a>
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<ul>
<li>Fei Gao, Yuhao Lin, Jiaqi Shi, Maoying Qiao, Nannan Wang*. AesMamba: Universal Image Aesthetic Assessment with State Space Models. The 32nd ACM Multimedia Conference (ACM MM), Accepted (Oral), 2024.</li>
<li>Fei Gao, Lingna Dai, Jingjie Zhu, Mei Du, Yiyuan Zhang, Maoying Qiao, Chenghao Xia, Nannan Wang, and Peng Li *， Human-Robot Interactive Creation of Artistic Portrait Drawings, 2024 IEEE International Conference on Robotics and Automation (ICRA), 11297-11304, May13-17, 2024, Yokohama, Japan. (* Corresponding Author)</li>
<li>Yu Chen *, Fei Gao *, Yanguang Zhang, Maoying Qiao, Nannan Wang**. Generating Handwritten Mathematical Expressions From Symbol Graphs: An End-to-End Pipeline. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), accepted, Seattle WA, USA, June 17-21, 2024. (* Equal Contributions)</li>
<li>Fei Gao, Yifan Zhu, Chang Jiang, Nannan Wang, Human-Inspired Facial Sketch Synthesis with Dynamic Adaptation, Proceedings of the International Conference on Computer Vision (ICCV), 7237-7247, 2023.</li>
<li>Biao Ma, Fei Gao* , Chang Jiang, Nannan Wang, Gang Xu, “Semantic-aware Generation of Multi-view Portrait Drawings,” the 32nd International Joint Conference on Artificial Intelligence (IJCAI), 1258-1266, 2023.</li>
<li>Chang Jiang, Fei Gao, Biao Ma, Yuhao Lin, Nannan Wang, Gang Xu, “Masked and Adaptive Transformer for Exemplar Based Image Translation,” *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 22418-22427.</li>
<li>Jun Yu, Xingxin Xu, Fei Gao, Shengjie Shi, Meng Wang, Dacheng Tao, Qingming Huang: Toward Realistic Face Photo-Sketch Synthesis via Composition-Aided GANs. IEEE Trans. Cybern. 51(9): 4350-4362 (2021)</li>
<li>Fei Gao, Xingxin Xu, Jun Yu, Meimei Shang, Xiang Li, Dacheng Tao: Complementary, Heterogeneous and Adversarial Networks for Image-to-Image Translation. IEEE Trans. Image Process. 30: 3487-3498 (2021)</li>
<li>Hanliang Jiang, Fuhao Shen, Fei Gao, Weidong Han: Learning efficient, explainable and discriminative representations for pulmonary nodules classification. Pattern Recognit. 113: 107825 (2021)</li>
<li>Lin Zhao, Meimei Shang, Fei Gao, Rongsheng Li, Fei Huang, Jun Yu: Representation learning of image composition for aesthetic prediction. Comput. Vis. Image Underst. 199: 103024 (2020)</li>
<li>Hanliang Jiang, Fei Gao, Xingxin Xu, Fei Huang, Suguo Zhu: Attentive and ensemble 3D dual path networks for pulmonary nodules classification. Neurocomputing 398: 422-430 (2020) </li>
<li>Fei Gao, Jingjie Zhu, Zeyuan Yu, Peng Li, Tao Wang: Making Robots Draw A Vivid Portrait In Two Minutes. IROS 2020: 9585-9591</li>
<li>Fei Gao, Jun Yu, Suguo Zhu, Qingming Huang, Qi Tian: Blind image quality prediction by exploiting multi-level deep representations. Pattern Recognit. 81: 432-442 (2018)</li>
<li>Fei Gao, Yi Wang, Panpeng Li, Min Tan, Jun Yu, Yani Zhu: DeepSim: Deep similarity for image quality assessment. Neurocomputing 257: 104-114 (2017)</li>
<li>Fei Gao, Jun Yu: Biologically inspired image quality assessment. Signal Process. 124: 210-219 (2016)</li>
<li>Fei Gao, Dacheng Tao, Xinbo Gao, Xuelong Li: Learning to Rank for Blind Image Quality Assessment. IEEE Trans. Neural Networks Learn. Syst. 26(10): 2275-2290 (2015)</li>
<li>Xinbo Gao, Fei Gao, Dacheng Tao, Xuelong Li: Universal Blind Image Quality Assessment Metrics Via Natural Scene Statistics and Multiple Kernel Learning. IEEE Trans. Neural Networks Learn. Syst. 24(12): 2013-2026 (2013)</li>
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