|
[1]
|
姜晓坤, 朱泓, 李志义. 新工科人才培养新模式[J]. 高教发展与评估, 2018, 34(2): 17-24.
|
|
[2]
|
陈健, 郑雅丹, 林丽. 新工科背景下面向学生的数字图像处理课程案例教学研究[J]. 计算机教育, 2026(4): 302-308.
|
|
[3]
|
何小海, 李鑫磊, 魏海涛, 毕晓东, 聂尧佳, 熊志娜, 张皓彦, 熊淑华. 深度学习驱动的视频编码: 方法、进展与展望[J]. 数据采集与处理, 2026, 41(2): 515-542.
|
|
[4]
|
万帅, 杨付正, 新一代视频压缩标准H.265/HEVC原理、标准与实现[M]. 北京: 电子工业出版社, 2014.
|
|
[5]
|
熊轲, 严祥康, 毛晋, 董瑞. 樊平毅基于自监督学习的多任务图像无源域适应语义通信框架[J/OL]. 北京航空航天大学学报, 1-15. 2026-06-02.[CrossRef]
|
|
[6]
|
张垚鑫, 张旻. 基于人工智能背景下应用型本科院校的“数字图像处理”课程教学改革[J]. 教育进展, 2024, 14(12): 1149-1154.
|
|
[7]
|
晏苏红, 谢于晨, 赵红霞. 人工智能赋能《信号与系统》课程教学改革研究[J]. 职业教育发展, 2025, 14(7): 181-187.
|
|
[8]
|
范迪, 孙慧敏, 周钰慧, 滕升华. 数字图像处理的“三融合、三驱动、三协同”课程教学创新实践[J]. 创新教育研究, 2024, 12(9): 271-277.
|
|
[9]
|
纪昕茹, 陈杰, 李鑫, 张博芝. AI赋能移动通信技术课程产教融合教学改革研究与实践[J]. 社会科学前沿, 2026, 15(3): 1-7.
|
|
[10]
|
Garg, A., Soodhani, K.N. and Rajendran, R. (2025) Enhancing Data Analysis and Programming Skills through Structured Prompt Training: The Impact of Generative AI in Engineering Education. Computers and Education: Artificial Intelligence, 8, Article ID: 100380. [Google Scholar] [CrossRef]
|
|
[11]
|
Khatry, K. and Samsami, R. (2025) Engineering Education in the Era of Generative Artificial Intelligence (Gen AI): Current State and Future Directions. 2025 ASEE Northeast Section Conference, Bridgeport, 22 March 2025.
|
|
[12]
|
Wang, T.Y., Li, F., Qiao, X.Y., et al. (2021) Low-Complexity Error Resilient HEVC Video Coding: A Deep Learning Approach. IEEE Transactions on Image Processing, 30, 1245-1260. [Google Scholar] [CrossRef] [PubMed]
|
|
[13]
|
Mei, Y., Li, L., Li, Z. and Li, F. (2022) Learning-Based Scalable Image Compression with Latent-Feature Reuse and Prediction. IEEE Transactions on Multimedia, 24, 4143-4157. [Google Scholar] [CrossRef]
|
|
[14]
|
Kuang, Z.Z., Bi, H.X., Li, F., Xu, C. and Sun, J. (2024) Polarimetry-Inspired Contrastive Learning for Class-Imbalanced PolSAR Image Classification. IEEE Transactions on Geoscience and Remote Sensing, 62, Article ID: 5212819. [Google Scholar] [CrossRef]
|
|
[15]
|
Kuang, Z.Z., Bi, H.X., Li, F. and Xu, C. (2025) ECP-Mamba: An Efficient Multiscale Self-Supervised Contrastive Learning Method with State Space Model for PolSAR Image Classification. IEEE Transactions on Geoscience and Remote Sensing, 63, Article ID: 5218718. [Google Scholar] [CrossRef]
|