🌞 Dengyang Jiang

Hi, there! I'm Dengyang Jiang, currently an undergraduate student from Northwestern Polytechnical University, supervised by Prof. Lei Zhang.

My research interests encompass deep learning and
computer vision, with a particular focus on visual generation and representation (diffusion model, self-supervised learning, the synergy between representation learning and generative modeling), unified model (unified multimodal understanding and generation model, visual generalist model), and reinforce learning (multimodal reinforce tuning).

I am delighted to communicate and collaborate with anyone interested in computer vision and deep learning. Feel free to contact me via Email or Xiaohongshu(RedNote).

profile photo

Education Experience

School of Automation, Northwestern Polytechnical University, 2022-2026
B.S.E., supervised by Prof. Lei Zhang, in the research team leaded by Prof. Yanning Zhang.

Work Experience

Intelligent Information, Alibaba Group, 2025.06-present
Research Intern, mentored by Dr. Peng Gao.
SGIT AI Lab, State Grid Corporation of China, 2024.06-2025.06
Research Intern, mentored by Prof. Mengmeng Wang, also work with Dr. Jingdong Wang.

Publications/Preprints (Google Scholar)

No Other Representation Component Is Needed: Diffusion Transformers Can Provide Representation Guidance by Themselves
Dengyang Jiang, Mengmeng Wang, Liuzhuozheng Li, Lei Zhang, Haoyu Wang, Wei Wei, Guang Dai, Yanning Zhang, Jingdong Wang
Preprint, 2025
paper / project page / code
Self-representation alignment for enhancing representation learning and generation performance of diffusion transformers.
AffordanceSAM: Segment Anything Once More in Affordance Grounding
Dengyang Jiang, Mengmeng Wang, Teli Ma, Hengzhuang Li, Yong Liu, Guang Dai, Lei Zhang
Preprint, 2025
paper
Transferring SAM to affordance grounding task and showing robust performance for both seen and unseen actions.
Low-Biased General Annotated Dataset Generation
Dengyang Jiang, Haoyu Wang, Lei Zhang, Wei Wei, Guang Dai, Mengmeng Wang, Jingdong Wang, Yanning Zhang
CVPR, 2025
paper / code
A low-biased general annotated dataset (e.g, ImageNet) generation framework helps to obtain more generalized visual backbones.

This web is is adapted from Jon Barron's website.