邮 件：sfzhang@ustc.edu.cn
所属单位：人工智能与数据科学学院
个人主页： https://sherrylone.github.io
主要研究方向：图像与三维场景表征学习、生成式模型、三维场景理解、多模态融合感知大模型、具身智能。
张少锋，预聘副教授，2025年博士毕业于上海交通大学计算机系，师从严骏驰教授。已在包括TPAMI、CVPR、ICCV、NeurIPS、ICML、ICLR等在内的国际顶级会议和期刊发表文章20余篇，其中一作/共一十余篇，并多次获得spotlight与oral。
近几年代表论著：
[1] Shaofeng Zhang, Qiang Zhou, Zhibin Wang, Hao Li, Junchi Yan. EasyOutPainter: One Step Image Outpainting with Both Continuous Multiple and Resolution, TPAMI 2025.
[2] Xiangdong Zhang*, Shaofeng Zhang*, Junchi Yan. Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views, ICCV 2025.
[3] Xiangdong Zhang*, Shaofeng Zhang*, Junchi Yan. PCP-MAE: Learning to Predict Centers for Point Masked Autoencoders, NeurIPS 2024.
[4] Shaofeng Zhang, Qiang Zhou, Sitong Wu, Haoru Tan, Zhibin Wang, Jinfa Huang, Junchi Yan. CR2PQ: Continuous Relative Rotary Positional Query for Dense Visual Representation Learning, ICLR 2025.
[5] Shaofeng Zhang, Jinfa Huang, Qiang Zhou, Zhibin Wang, Fan Wang, Jiebo Luo, Junchi Yan. Continuous-Multiple Image Outpainting in One-Step via Positional Query and A Diffusion-based Approach, ICLR 2024
[6] Shaofeng Zhang, Qiang Zhou, Zhibin Wang, Fan Wang, Junchi Yan. Patch-level Contrastive Learning via Positional Query for Visual Pre-training, ICML 2023.
[7] Shaofeng Zhang, Feng Zhu, Rui Zhao, Junchi Yan. Patch-Level Contrasting without Patch Correspondence for Accurate and Dense Contrastive Representation Learning, ICLR 2023.
[8] Shaofeng Zhang, Feng Zhu, Rui Zhao, Junchi Yan. Contextual Image Masking Modeling via Synergized Contrasting without View Augmentation for Faster and Better Visual Pretraining, ICLR 2023.
[9] Shaofeng Zhang, Meng Liu, Junchi Yan, Hengrui Zhang, Lingxiao Huang, Pinyan Lu, Xiaokang Yang. M-Mix: Generating Hard Negatives via Multi-sample Mixing for Contrastive Learning, SIGKDD 2022.
[10] Shaofeng Zhang, Lyn Qiu, Feng Zhu, Junchi Yan, Hengrui Zhang, Rui Zhao, Hongyang Li, Xiaokang Yang. Align Representations with Base: A New Approach to Self-Supervised Learning, CVPR 2022.
招生信息：
本课题组常年招收本科实习生、硕士与博士研究生，主要研究方向包括（但不限于）：表征学习、多模态感知理解、具身智能等。
我们能够为你提供充足的算力支持、丰富的研究思路，并推荐科大、交大、复旦等高校的硕博深造机会，以及知名互联网企业的实习或全职岗位。
欢迎有意者准备个人简历（包含GPA、排名、AI相关课程及项目经历）发送至邮箱：sfzhang@ustc.edu.cn。