Yunuo Chen

Shanghai Jiao Tong University

Yunuo Chen 陈予诺

Ph.D. student in Computer Science

I am a Ph.D. student at SJTU Media Lab, Shanghai Jiao Tong University, advised by Wen Gao, an Academician of the Chinese Academy of Engineering, and Prof. Guo Lu. I received my B.Eng. in Electrical and Computer Engineering from the UM-SJTU Joint Institute, Shanghai Jiao Tong University.

My research focuses on Visual Compression, AIGC and World Model.

News

  • Two papers were accepted to NeurIPS 2026.
  • Two papers were accepted to TCSVT 2026.
  • One paper was accepted to ECCV 2026.
  • Two papers were accepted to CVPR 2026.
  • Two papers were accepted to ICLR 2026.
  • One paper was accepted to NeurIPS 2025.
  • One paper was accepted to ICCV 2025 as an Oral presentation.
  • One paper was accepted to ICLR 2024.
  • One paper was accepted to ICML 2023.

Experience

2026.02 – Present

Research Intern

Microsoft Research Asia (MSRA)

Supervised by Fangyun Wei and Dong Chen.

Topic: lightweight text-to-image and image-editing foundation models.

2025.07 – 2025.12

Research Intern

StepFun (阶跃星辰)

Supervised by Wei Cheng.

Topic: multi-reference and many-to-many image generation.

Education

  • Ph.D. in Computer Science, Shanghai Jiao Tong University
  • B.Eng. in Electrical and Computer Engineering, UM-SJTU Joint Institute, Shanghai Jiao Tong University
  • Undergraduate Minors: Data Science, Computer Science, and Global China Studies.

Publications

Selected first-author or representative works are highlighted below. Equal contribution is marked by *.

ICLR 2026

Content-Aware Mamba for Learned Image Compression

Yunuo Chen, Zezheng Lyu, Bing He, Hongwei Hu, Qi Wang, Yuan Tian, Li Song, Wenjun Zhang, Guo Lu

Builds a content-aware state-space model for image compression by combining content-adaptive token permutation with sample-specific global prior injection.

ICCV 2025 Oral

Knowledge Distillation for Learned Image Compression

Yunuo Chen*, Zezheng Lyu*, Bing He, Ning Cao, Gang Chen, Guo Lu, Wenjun Zhang

Develops a stage-wise modular distillation framework for learned image compression, reducing model complexity while preserving rate-distortion performance.

Services

  • Conference Reviewer: CVPR 25/26, ICCV 25, ICLR 25/26/27, ECCV 26, ICML 26, NeurIPS 25/26, AAAI 26/27, BMVC 26
  • Journal Reviewer: IEEE TIP, IEEE TCSVT, ACM TOMM, IEEE Signal Processing Magazine
  • Workshop Organizer: VCIP 2025 Ultra-Low Bitrate Video Compression Challenge

Honors and Awards

  • Outstanding Graduate, Shanghai Jiao Tong University
  • Yu Liming Scholarship, UM-SJTU Joint Institute
  • Undergraduate Outstanding Scholarship, UM-SJTU Joint Institute