王润琪

基本信息

教育背景

2014年-2018年 中国矿业大学 本科

2018年-2020年 北京航空航天大学 硕士

2020年-2024年 北京航空航天大学 博士

工作经历

研究方向

• 人工智能
• 机器学习与认知计算
• 生成式人工智能与安全
• 新一代电子信息技术
• 大数据技术与工程
• 计算机技术
• 软件工程
• 低空航行安全保障

招生专业

• 计算机科学与技术硕士
• 人工智能硕士
• 计算机技术硕士
• 新一代电子信息技术（含量子技术等）硕士
• 大数据技术与工程硕士
• 低空技术与工程硕士
• 软件工程硕士

科研项目

教学工作

研究生课程：机器学习 Machine Learning 

本科生课程：计算机系统导论

论文/期刊

部分发表论文如下（*通讯作者，†共同一作）：

[1] R. Wang, L. Yang, H. Chen, et al. Anti-bandit for neural architecture search[J]. International Journal of Computer Vision, 2023, 131(10): 2682-2698. (SCI 1区，影响因子19.5)

[2] R. Wang, Z. Liu, B. Zhang, et al. Few-Shot Learning with Complex-Valued Neural Networks and Dependable Learning [J]. International Journal of Computer Vision, 2023, 131(1):385-404. (SCI 1区，影响因子19.5)

[3] R. Wang, X. Duan, G. Kang, et al. Attriclip: A non-incremental learner for incremental knowledge learning[C]. In the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, Canada, 2023: 3654-3663. (CCF A)

[4] R. Wang， H. Zheng， X. Duan，et al. Few-shot learning with visual distribution calibration and cross-modal distribution alignment, In the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, Canada, 2023. (CCF A)

[5] R. Wang, H. Sun, L. Yang, et al. AQ-DETR: Low-Bit Quantized Detection Transformer with Auxiliary Queries[C]. In the Proceedings of Association for the Advancement of Artificial Intelligence (AAAI), Vancouver, Canada, 2024: 15598-15606. (CCF A)

[6] R. Wang, Y. Bao, B. Zhang, et al. Anti-retroactive Interference for Lifelong Learning[C]. In the Proceedings of the European Conference on Computer Vision (ECCV), Tel Aviv, Israel, 2022: 163-178.（CCF B）

[7] W. Cao†, R. Wang†, X. Duan, et al. Parameter-Efficient Semantic Augmentation for Enhancing Open-Vocabulary Object Detection [C], In the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026. (CCF A)

[8] B. Zhang†, R. Wang†, X. Wang, et al. Modulated convolutional networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2025: 1-14. （SCI 1区,影响因子10.2）

[9] H. Zheng†, R. Wang†,  J. Liu, et al. Cross-level distillation and feature denoising for cross-domain few-shot classification[C]. In Proceedings of the International Conference on Learning Representation (ICLR), Kigali, Rwanda, 2023: 1-14. （机器学习国际顶会）

[10] S. Xue†, R. Wang†, B. Zhang, et al. IDARTS: Interactive differentiable architecture search[C]. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Virtual Event, 2021: 1163-1172.（CCF A）

[11] R. Xu, R. Wang*, Y. Zhang, et al. TLMA: Mitigating the Impact of Weakly Labeled Information for Video Anomaly Detection[C], In the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026. (CCF A)

[12] Z. Wang, Y. Yang, B. Zhang, R. Wang*, et al. Enhancing multi-task performance through associative adversarial learning based on selective attacks[J], Neurocomputing, 2025, 640:130229. (SCI 2区)

[13] X. Wang, J. Zheng, R. Wang*, et al, Memory-Aware Replay and Loss Balance for Long-Tailed Class Incremental Learning with Vision-Language Models[C], International Conference on Computational Visual Media, 2026.

[14] S. Wu, Y. Wang, X. Liu, Y. Yang, R. Wang*, DFM: Differentiable Feature Matching for Anomaly Detection[C]. In the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. (CCF A)

[15] H. Sun, R. Wang, Y. Li, et al. SET: Spectral Enhancement for Tiny Object Detection[C]. In the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, United States, 2025: 4713-4723.

[16] J. Zhao, S. Xu, R. Wang, et al. Data-adaptive binary neural networks for efficient object detection and recognition[J]. Pattern Recognition Letters, 2022, 153: 239-245.

[17] R. Wang, W. Wang, T. Ma, et al. An adaptive gradient method with differentiation element in deep neural networks[C]. In the Proceedings of the IEEE Conference on Industrial Electronics and Applications (ICIEA), Virtual Event, 2020: 1582-1587.

[18] S. Gao, R. Wang, L. Jiang, et al. 1-bit WaveNet: Compressing a Generative Neural Network in Speech Recognition with Two Binarized Methods[C]. In the Proceedings of the IEEE Conference on Industrial Electronics and Applications (ICIEA), Virtual Event, 2021: 2043-2047.

[19] S. Xue, B. Zhao, H. Chen, R. Wang, et al. UCB-ENAS based on Reinforcement Learning[C]. In the Proceedings of the IEEE Conference on Industrial Electronics and Applications (ICIEA), Virtual Event, 2021: 2008-2013.

[20] 张宝昌，鲍宇翔，王润琪，等. 基于协同梯度下降的可信学习方法[J]. 中国科学：技术科学，2024，54(2): 257-264.

专著/译著

专利

1. 自适应学习率协同优化的目标识别方法、装置及电子设备

2.⼀种基于联想学习的选择性攻击⽅法及装置

3.图像分类方法、装置、电子设备、存储介质和程序产品

软件著作权

获奖与荣誉

入选博士后创新人才计划；

入选北京交通大学青英人才培育计划；

获得博士国家奖学金；

北京航空航天大学优秀毕业生；

CCF计算机应用产品技术奖；

CVPR基础大模型比赛十佳

社会兼职

担任《信息与控制期刊》青年编委；

担任《IEEE Transactions on Multimedia》、《Machine Vision and Applications》、《IEEE Network Magazine》等期刊审稿人；

担任CVPR，ICCV，ECCV，AAAI，NuerIPS、ACM MM等会议PC Member；

CAAI学会会员 机器学习专委会通信委员