Shanghua Gao
Summary
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RockstarShanghua Gao is a postdoctoral fellow at Harvard with nine years of research experience in computer vision, AIGC, architecture design, and representation learning, earned during a PhD at Nankai University under Prof. Ming-Ming Cheng. He has authored 10+ papers in top venues including TPAMI and CVPR, amassed over 2,600 citations—most notably Res2Net (PAMI 2021) which has 1,400+ citations—and has seen several research contributions shipped into industrial products. At Sea AI Lab and YITU Tech he translated academic advances into practical models, and on GitHub he actively maintains the Res2Net pretrained-models repository, expanding model variants and weights used widely by the community. Based in Boston, he combines deep theoretical insight with hands-on engineering to move multi-scale backbone architectures from paper to production.
10 years of coding experience
Github Skills (9)
computer-vision10
pytorch10
machine-learning10
deep-learning10
pre-trained-model10
modeling9
trainings9
continuous-deployment8
ml-deployment8
Programming languages (7)
Github contributions (5)
Res2Net/Res2Net-PretrainedModels
Jul 2019 - Dec 2022
(ImageNet pretrained models) The official pytorch implemention of the TPAMI paper "Res2Net: A New Multi-scale Backbone Architecture"
Role in this project:
ML Engineer
ML EngineerContributions:39 commits, 1 PR, 38 pushes in 3 years 5 months
Contributions summary:Shanghua primarily contributed to the project by modifying and adding pretrained models, specifically focusing on Res2Net and related architectures. Their commits involve updating download links for model weights and introducing a new version of the Res2Net model (v1b) with corresponding code changes. They also made updates to include other models such as dla and res2next. These changes suggest an active role in maintaining and expanding the available pre-trained models within the repository.
imagenetpre-trained-modelpytorchres2netbackbone
Shanghua-Gao/RBN
Apr 2021 - Sep 2022
The official repo of the CVPR2021 oral paper: Representative Batch Normalization with Feature Calibration
Contributions:1 release, 8 commits, 6 pushes in 1 year 5 months
batch-normalization
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