Fengyu Yang

Hi! This is Fengyu Yang, a senior undergraduate at University of Michigan College of Engineering majoring in Computer Science, advised by Prof. Andrew Owens. I also spent a wonderful summer last year at Zhejiang University advised by Prof. Xi Li.

My undergraduate research interest mainly lies in Computer Vision, particularly in multimodal perception, continue learning and semantic segmentation.

Email:fredyang at umich dot edu

I am currently applying for Ph.D. in Computer Science for Fall 2023!

Email  /  Github

profile photo

News

  • 2022/12: Selected as the Runner Up of the CRA Outstanding Undergraduate Researcher Award.

  • 2022/11: One paper submitted to CVPR 2023.

  • 2022/09: "Touch and Go: Learning from Human-Collected Vision and Touch" accepted by NeurIPS 2022.

  • 2022/07: "RBC: Rectifying the Biased Context in Continual Semantic Segmentation" accepted by ECCV 2022.

  • 2022/03: "Sparse and Complete Latent Organization for Geospatial Semantic Segmentation" accepted by CVPR 2022.

  • 2021/12: Accpet my first invitation to be a reviewer in CVPR 2022.

  • 2021/11: Two papers submitted to CVPR 2022.


Publications

touch-go  Touch and Go: Learning from Human-Collected Vision and Touch

Fengyu Yang*, Chenyang Ma*, Jiacheng Zhang, Jing Zhu, Wenzhen Yuan, Andrew Owens
NeurIPS (Datasets and Benchmarks Track), 2022
project page / paper / dataset / code

A dataset of paired vision-and-touch data collected by humans. We apply it to: 1) restyling an image to match a tactile input, 2) self-supervised representation learning, 3) multimodal video prediction.

touch-go  RBC: Rectifying the Biased Context in Continual Semantic Segmentation

Hanbin Zhao*, Fengyu Yang*, Xinghe Fu, Xi Li
ECCV, 2022
paper / code

We first consider the biased context in continue semantic segmentation (CSS) and propose a context-rectified image-duplet learning scheme and a biased-context-insensitive consistency loss to tackle CSS problem.

touch-go  Sparse and Complete Latent Organization for Geospatial Semantic Segmentation

Fengyu Yang*, Chenyang Ma*,
CVPR, 2022
paper

We propose a prototypical contrastive learning method using both foreground and background categories to tackle the large intra-class variance in geospatial semantic segmentation.

Honors and Awards

  • CRA Outstanding Undergraduate Researcher Award (Runner Up), Computing Research Association. December 2022.
  • Wang Chu Chien-Wen Research Award, University of Michigan. April 2022.
  • Henry Ford II Prize, University of Michigan. March 2022.
  • EECS Scholar, University of Michigan. 2021-2022.
  • James B. Angell Scholar, University of Michigan. 2021-2022.
  • Dean's List, University of Michigan. 2019-2022.
  • University Honors, University of Michigan. 2019-2022.

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