People

Min-Hung Chen

Min-Hung (Steve) Chen is a Staff Research Scientist at NVIDIA Research Taiwan, working on Vision+X Multimodal AI. He received his Ph.D. degree from Georgia Tech, advised by Prof. Ghassan AlRegib and in collaboration with Prof. Zsolt Kira. Before joining NVIDIA, Min-Hung 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.

Min-Hung's research interest is mainly Multimodal AI, including Vision-Language, 4D (video+depth) Understanding, Efficient Deep Learning, VLA, and Transformer. He is also interested in Learning without Fully Supervision, including domain adaptation, transfer learning, X-supervised learning, etc. Please visit his website for more information.

[Personal Website] [LinkedIn] [Twitter]

Research Area(s)
Applied Perception
Artificial Intelligence and Machine Learning
Computer Vision
Main Field of Interest
Computer Vision
Google Scholar
https://scholar.google.com/citations?user=ovzuxi8AAAAJ

Publications

2025

ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent Planning
Chi-Pin Huang, Yueh-Hua Wu, Min-Hung Chen, Frank Wang, Fred Yang
Neural Information Processing Systems (NeurIPS) 2025
Hymba: A Hybrid-head Architecture for Small Language Models
Xin Dong, Yonggan Fu*, Shizhe Diao, Wonmin Byeon, Zijia Chen, Ameya Sunil Mahabaleshwarkar, Shih-Yang Liu, Matthijs Van keirsbilck, Min-Hung Chen, Yoshi Nishi, Yingyan Celine Lin, Jan Kautz, Pavlo Molchanov
Hymba - ICLR 2025
ICLR spotlight paper
Semantic Prompt Learning for Weakly-Supervised Semantic Segmentation
Ci-Siang Lin, Chien-Yi Wang, Frank Wang, Min-Hung Chen
Winter Conference on Applications of Computer Vision (WACV)
Spatio-Temporal Context Prompting for Zero-Shot Action Detection
Wei-Jhe Huang, Min-Hung Chen, Shang-Hong Lai
Winter Conference on Applications of Computer Vision (WACV)
CorrFill: Enhancing Faithfulness in Reference-based Inpainting with Correspondence Guidance in Diffusion Models
Kuan-Hung Liu, Cheng-Kun Yang, Min-Hung Chen, Yu-Lun Liu, Yen-Yu Lin
Winter Conference on Applications of Computer Vision (WACV)

2024

Diffusion-Reward Adversarial Imitation Learning
Chun-Mao Lai, Hsiang-Chun Wang, Ping-Chun Hsieh, Frank Wang, Min-Hung Chen, Shao-Hua Sun
Neural Information Processing Systems (NeurIPS)
DoRA: Weight-Decomposed Low-Rank Adaptation
Shih-Yang Liu, Chien-Yi Wang, Hongxu Danny Yin, Pavlo Molchanov, Frank Wang, Kwang-Ting Cheng, Min-Hung Chen
International Conference on Machine Learning (ICML) 2024

2023

2D-3D Interlaced Transformer for Point Cloud Segmentation with Scene-Level Supervision
Cheng-Kun Yang, Min-Hung Chen, Yung-Yu Chaung, Yen-Yu Lin
IEEE/CVF International Conference on Computer Vision (ICCV) 2023