研究方向与介绍
计算机视觉（目标检测与跟踪、人群计数、视线估计、服装编辑），三维点云（法向估计、点云生成），数字人，多模态大模型，图像生成与多视角合成
本人长期致力于深度学习、多模态学习、大模型与计算机视觉等人工智能现代算法与应用研究，以人工智能“理论+应用”为主线，提出了一系列高性能视觉学习模型，提升了学习模型在各类视觉应用场景下的鲁棒性和可迁移性。目前，已在图像分类，目标检测、无人机巡检、目标跟踪、图像与视频编解码、AIGC等多视觉任务中取得了系列成果，相关成果已发表于国际高水平会议或期刊（如TMM，ACM MM，ICME，WACV，Pattern Recognition，CGF, CAD, Neurocomputing等），多项研究已在实际场景中推广应用，与国内知名高校企业，如浙江大学、南京大学、字节跳动等有广泛合作。欢迎对人工智能感兴趣并热衷于研究（应用）的同学报考。
教育经历
2021年09月-2022年10月 加拿大圭尔夫大学，计算机科学，博士
2020年11月-2021年05月 西湖大学深度学习实验室访学
2017年09月-2021年04月 加拿大纽芬兰纪念大学， 计算机科学，博士
2014年09月-2017年01月 天津大学，软件工程，硕士
2010年09月-2014年06月 浙江理工大学，信息与计算科学，学士
发表论文
谷歌学术：https://scholar.google.com/citations?user=MfsLOqcAAAAJ&hl=zh-CN
[1] Wang, M., Li, Y., Zhou, J., Taylor, G. W., & Gong, M. (2024). GCNet: Probing Self-similarity Learning for Generalized Counting Network. Pattern Recognition (PR), 153, 110513.（SCI，Top期刊）
[2] Wang, M., Cai, H., Han, X. F., Zhou, J., & Gong, M. (2023). STNet: Scale Tree Network with Multi-level Auxiliator for Crowd Counting. IEEE Transactions on Multimedia (TMM), 25, 2074-2084.（SCI，Top期刊）
[3] Wang, M., Zhou, J., Cai, H., & Gong, M. (2023). CrowdMLP: Weakly-supervised Crowd Counting via Multi-granularity MLP. Pattern Recognition (PR), 144, 109830.（SCI，Top期刊）
[4] Wang, M., Cai, H., Zhou, J., & Gong, M. (2021). Interlayer and Intralayer Scale Aggregation for Scale-invariant Crowd Counting. Neurocomputing, 441, 128-137.（SCI，Top期刊）
[5] Wang, M., Cai, H., Dai, Y., & Gong, M. (2023). Dynamic Mixture of Counter Network for Location-Agnostic Crowd Counting. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (pp. 167-177).（CORE A会议）
[6] Wang, M., Cai, H., Zhou, J., & Gong, M. (2020). Stochastic Multi-scale Aggregation Network for Crowd Counting. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 2008-2012).（Oral）（CCF B会议）
[7] Wang, M., Cai, H., Huang, X., & Gong, M. (2020). ADNet: Adaptively Dense Convolutional Neural Networks. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (pp. 1001-1010).（CORE A会议）
[8] Wang, M., Zhou, J., Mao, W., & Gong, M. (2019). Multi-scale Convolution Aggregation and Stochastic Feature Reuse for DenseNets. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (pp. 321-330).（CORE A会议）
[9] Wang, M., Yuan, S., Li, Z., Zhu, L., Buys, E., & Gong, M. (2024). Language-Guided Zero-Shot Object Counting. In Proceedings of the IEEE International Conference on Multimedia and Expo Workshops (ICMEW) (pp. 1-6).
[10] Wang, M., Zhou, J., Dai, Y., Buys, E., & Gong, M. (2024). Enhancing Zero-shot Counting via Language-guided Exemplar Learning. IEEE Transactions on Multimedia (TMM).（SCI，Top期刊，审稿阶段）
[11] Li, Y., Wang, M., Gong, M, Lu, Y., & Liu L. (2024). FER-former: Multi-modal Transformer for Facial Expression Recognition. IEEE Transactions on Multimedia (TMM).（SCI，Top期刊，已接收）
[12] Cai, H., Wang, M.*, Mao, W., & Gong, M. (2020). No-reference Image Sharpness Assessment based on Discrepancy Measures of Structural Degradation. Journal of Visual Communication and Image Representation (JVCIR), 71, 102861.（SCI期刊）
[13] Hou, J., Lu, Y., Wang, M., Ouyang, W., Yang, Y., Zou, F., ... & Liu, Z. (2024). A Markov Chain Approach for Video-based Virtual Try-on with Denoising Diffusion Generative Adversarial Network. Knowledge-Based Systems (KBS), 300, 112233.（SCI，Top期刊）
[14] Huang, X., Wang, M., & Gong, M. (2019). Hierarchically-fused Generative Adversarial Network for Text to Realistic Image Synthesis. In Proceedings of the Conference on Computer and Robot Vision (CRV) (pp. 73-80).（获得最佳论文奖）
[15] Xu, J., Tang, B., Wang, M., Li, M., & Ma, M. (2023). CPNet: Exploiting CLIP-based Attention Condenser and Probability Map Guidance for High-fidelity Talking Face Generation. In Proceedings of the IEEE International Conference on Multimedia and Expo (ICME) (pp. 240-245).（CCF B会议）
[16] Xie, T., Liao, L., Bi, C., Tang, B., Yin, X., Yang, J., Wang, M., ... & Ma, Z. (2021). Towards Realistic Visual Dubbing with Heterogeneous Sources. In Proceedings of the ACM International Conference on Multimedia (ACM MM) (pp. 1739-1747).（CCF A会议）
[17] Zhou, J., Jin, W., Wang, M.*, Liu, X., Li, Z., & Liu, Z. (2023). Improvement of Normal Estimation for Point Clouds via Simplifying Surface Fitting. Computer-Aided Design (CAD), 161, 103533.（SCI期刊）
[18] Zhou, J., Jin, W., Wang, M.*, Liu, X., Li, Z., & Liu, Z. (2022). Fast and Accurate Normal Estimation for Point Cloud via Patch Stitching. Computer-Aided Design (CAD), 142, 103121.（SCI期刊）
[19] Mao, W., Wang, M., Huang, H., & Gong, M. (2022). A Robust Framework for Multi-view Stereopsis. The Visual Computer (TVC), 38(5), 1539-1551.（SCI期刊）
[20] Zhou, J., Wang, M., Mao, W., Gong, M., & Liu, X. (2020). SiamesePointNet: A Siamese Point Network Architecture for Learning 3D Shape Descriptor. Computer Graphics Forum (CGF), 39(1), 309-321.（SCI期刊）
