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Hi there πŸ‘‹

My name is Min-Hung (Steve) Chen (ι™³ζ•εΌ˜ in Chinese). I am a Senior Research Scientist at NVIDIA Research Taiwan, working on Vision+X Multi-Modal AI. I received my Ph.D. degree from Georgia Tech, advised by Prof. Ghassan AlRegib and in collaboration with Prof. Zsolt Kira. Before joining NVIDIA, I was working on Biometric Research for Cognitive Services as a Research Engineer II at Microsoft Azure AI, and was working on Edge-AI Research as a Senior AI Engineer at MediaTek, respectively.

My research interest is mainly Multi-Modal AI, including Vision-Language, Video Understanding, Cross-Modal Learning, Efficient Tuning, and Transformer. I am also interested in Learning without Fully Supervision, including domain adaptation, transfer learning, continual learning, X-supervised learning, etc.

[Update] I released a comprehensive paper list for Vision Transformer & Attention to facilitate related research. Feel free to check it (I would be appreciative if you can β˜…STAR it)!

[Personal Website][LinkedIn][Twitter][Google Scholar][Resume]

Min-Hung (Steve)'s GitHub stats

Min-Hung (Steve) Chen's Projects

da icon da

Unsupervised Domain Adaptation Papers and Code

deepcoral icon deepcoral

A PyTorch implementation of 'Deep CORAL: Correlation Alignment for Deep Domain Adaptation.', ECCV 2016

leetcode icon leetcode

:pencil: Python / C++ 11 Solutions of All 468 LeetCode Questions

openpose icon openpose

OpenPose: A Real-Time Multi-Person Keypoint Detection And Multi-Threading C++ Library

sstda icon sstda

[CVPR 2020] Action Segmentation with Joint Self-Supervised Temporal Domain Adaptation (PyTorch)

ta3n icon ta3n

[ICCV 2019 (Oral)] Temporal Attentive Alignment for Large-Scale Video Domain Adaptation (PyTorch)

transferlearning icon transferlearning

Resources and codes about transfer learning and domain adaptation--迁移学习

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