无码av

教育经历：

• 2007.9-2011.6 重庆大学 本科

• 2011.9-2014.1 无码av 硕士

• 2014.7-2019.3 悉尼科技大学 博士

工作经历：

• 2019.05-2021.04 卡内基梅隆大续写 博士后

• 2021.05-2021.10 德克萨斯大学奥斯汀分校 博士后

• 2022.02-至今 无码av 教授

研究生课程：

数字图像处理与应用

机器学习

本科课程：

人工智能导论

人工智能；深度学习；计算机视觉；多模态学习

获奖情况：

入选全球前2%顶尖科学家榜单；AI 2000 国际最具影响力学者提名等

代表性论文：

• [1]Kang, G., Jiang, L., Wei, Y., Yang, Y., & Hauptmann, A. (2020). Contrastive adaptation network for single-and multi-source domain adaptation. IEEE transactions on pattern analysis and machine intelligence (T-PAMI), 44(4), 1793-1804.

• [2] Kang, G., Li, J., & Tao, D. (2017). Shakeout: A new approach to regularized deep neural network training. IEEE transactions on pattern analysis and machine intelligence (T-PAMI), 40(5), 1245-1258.

• [3] Kang, G., Wei, Y., Yang, Y., Zhuang, Y., & Hauptmann, A. (2020). Pixel-level cycle association: A new perspective for domain adaptive semantic segmentation. Advances in neural information processing systems (NeurIPS oral), 33, 3569-3580.

• [4] Liu, H., Cui, S., Cao, H., Ma, S., Wu, K., & Kang, G. (2026). A Style is Worth One Code: Unlocking Code-to-Style Image Generation with Discrete Style Space. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR oral) (pp. 1-10).

• [5] Wang, J., & Kang, G. (2025). Reclip++: Learn to rectify the bias of clip for unsupervised semantic segmentation. International Journal of Computer Vision, 133(11), 7946-7968.