个人简介： 宁阳，博士，副研究员，硕士生导师，兼任英国奥斯特大学博士生导师。2020年于山东大学获计算机科学与技术专业工学博士学位。长期系统从事计算机视觉、开放环境下多模态协同感知、多模态大模型及因果推理等方向的基础与应用研究。作为项目负责人主持山东省自然科学基金面上项目、山东建筑大学博士基金项目、军工类横向课题项目等，作为核心研究人员参与国家自然科学基金面上项目及山东省自然科学基金重大基础研究等项目。迄今，以第一作者或通讯作者在IEEE TMM、IEEE TCSVT、ACM TOMM、IJCAI、ACM MM等国际高水平期刊及顶级会议上发表论文20余篇。长期担任IEEE TMM、IEEE TNNLS、IEEE TCSVT等国际期刊，以及CVPR、ICCV、AAAI、ACM MM等人工智能领域顶级会议的审稿人。此外，现任山东省人工智能学会理事，以及中国计算机学会（CCF）人工智能与模式识别专委会、计算机视觉专委会、计算机应用专委会委员，积极参与学术组织与服务。入选山东省高等学校青创科技计划创新团队核心成员，并获得山东省人工智能科学技术奖二等奖。
所在团队、研究中心： 山东建筑大学模式识别与机器学习实验室、数智建筑与智慧运维省级工程研究中心
招生信息：
常年计划招收学术型硕士1名（优先录取）、专业型硕士1名；另外，招收本科生科研助手1-2名，有意向在本科就读阶段接触高水平科研并发表高水平学术论文可咨询联系。
代表性论文： 贡献1：开放环境下弱监督视觉理解的理论与方法 [1] Fang Jingyuan, Ning Yang*, Nie Xiushan, Liu Xinfeng, Cheng Zhiyong. VLHP: Learning Discriminative Vision-Language Hybrid Prototypes for Weakly Supervised Semantic Segmentation. ACM International Conference on Multimedia (ACM MM, CCF-A). 2025: 2939-2948. (通讯作者) [2] Bi Yanchao, Ning Yang*, Nie Xiushan, Lu Xiankai, Gong Yongshun, Li Leida. Towards Region-Adaptive Feature Disentanglement and Enhancement for Small Object Detection. International Joint Conference on Artificial Intelligence (IJCAI, CCF-A). 2025: 693-701. (通讯作者) [3] Fang Jingyuan, Ning Yang*, Nie Xiushan. Spatial Frequency-Aware Self-Distillation for Weakly-Supervised Semantic Segmentation. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2025. (通讯作者) [4] Chen Penyu, Nie Xiushan, Ning Yang, Zhang Yitian. Learning Efficient and Adaptive Cross-Channel Dependencies for Weakly-Supervised Object Detection. IEEE Transactions on Multimedia. 2025, 27: 8954-8966. [5] Bi Yanchao, Ning Yang, Nie Xiushan. Delving Into Coarse-Fine Feature Interaction Alignment for UAV Object Detection. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2025. 贡献2：面向开放场景的鲁棒视觉基础模型与应用 [1] Zhang Qiuyue, Zhang Yunfeng, Bao Fangxun, Ning Yang, Zhang Caiming. Graph-based stock prediction with multisource information and relational data fusion. Information Sciences. 2025, 690: 121561. [2] Bi Yanchao, Ning Yang*, Nie Xiushan, Lu Xiankai, Zhang Ruiheng, Zhang Huanlong. FGHDet: Delving Into Fine-grained Features With Head Selection for UAV Object Detection. Journal of Computer Science and Technology. 2025, 40: 1301-1315. (通讯作者) [3] Zhang Hao, Liu Meng, Qi Yuan, Ning Yang, Hu Shunbo, Nie Liqiang, Zhang Wenyin. Efficient brain tumor segmentation with lightweight separable spatial convolutional network. ACM Transactions on Multimedia Computing, Communications and Applications. 2024, 20(7): 1-19. [4] Ning Yang, Zhang Shouyi, Zhong Wei, Liu Peide, Zhang Caiming. A hybrid cross-scale transformer architecture for robust medical image segmentation. IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2022: 1677-1682. [5] Ning Yang, Zhang Shouyi, Xi Xiaoming, Guojie, Liu Peide, Zhang Caiming. CAC-EMVT: Efficient Coronary Artery Calcium Segmentation with Multi-Scale Vision Transformers. IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2021: 1462-1467. 贡献3：医学影像的智能分析 [1] Bao Fangxun, Zhao Yongqi, Zhang Xinyue, Zhang Yunfeng, Ning Yang. SARC-UNet: A coronary artery segmentation method based on spatial attention and residual convolution. Computer Methods and Programs in Biomedicine. 2024, 255: 108353. [2] Ning Yang, Wang Dawei, Tan Tianmin, Zhang Caiming. DCA: Densely cross-scale attention network for anatomically-plausible medical image segmentation. IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2023: 2153-2158. [3] Ning Yang, Zhang Yunfeng, Li Xuemei, Zhang Caiming. Towards accurate coronary artery calcium segmentation with multi‐scale attention mechanism. IET Image Processing. 2021, 15(6): 1359-1370. [4] Ning Yang, Han Zhongyi, Zhong Li, Zhang Caiming. DRAN: Deep recurrent adversarial network for automated pancreas segmentation. IET Image Processing. 2020, 14(6): 1091-1100. [5] Ning Yang, Liu Yifang, Zhang Yunfeng, Zhang Caiming. Adaptive image rational upscaling with local structure as constraints. Multimedia Tools and Applications. 2019, 78(6): 6889-6911. |
