Shihua Huang (黄世华)

Shihua Huang is currently a PhD candidate with the Dept. of Computer Science and Engineering at Michigan State University, East Lansing, USA.

His research interests are in the field of representation learning, notably neural architecture search, deep learning assisted evolutionary algorithms, and in particular dense image prediction (auto-driving and medical image analysis) and robust models.

Email  /  CV  /  Bio  /  Google Scholar  /  Github

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Selected Research
PontTuset FaPN: Feature-aligned Pyramid Network for Dense Image Prediction
Shihua Huang, Zhichao Lu, Ran Cheng, Cheng He
ICCV, 2021  
project page / arXiv / code

FaPN is a simple yet effective top-down pyramidal architecture to generate multi-scale features for dense image prediction. It improves FPN's AP / mIoU by 1.5 - 2.6% on all tasks. It achieved 56.7% mIoU over ADE20k-150 when paired with MaskFormer.

PontTuset RelativeNAS: Relative Neural Architecture Search via Slow-Fast Learning
Hao Tan, Ran Cheng, Shihua Huang, Cheng He, Changxiao Qiu, Fan Yang, Ping Luo
TNNLS , 2021  
arXiv / code
PontTuset Evolutionary Multi-Objective Optimization Driven by Generative Adversarial Networks (GANs)
Cheng He, Shihua Huang, Ran Cheng, Kay Chen Tan, Yaochu Jin
TCYB , 2020  
arXiv / code
Reviewer: IEEE Trans. on Multi Media, IEEE Trans. on Cognitive and Developmental Systems, Applied Soft Computing, and Complex & Intelligent Systems.