Henry Hengyuan Zhao

Hi 👋, I'm Henry, a PhD student in the Show Lab at the National University of Singapore, advised by Prof. Mike Zheng Shou.

I train Large Multimodal Models (LMMs) and develop agents/benchmarks for GUI automation and chart-to-code.

Selected work: agent–computer interaction (WorldGUI), large multimodal model training (Genixer, LOVA3), and interactive alignment/evaluation (InterFeedback).

Email GitHub Google Scholar @ZHHHYuan

📢 News

[08/2025] InterFeedback accepted to EMNLP 2025 Findings.
[03/2025] InterFeedback accepted to ICLR 2025 Bi-Align Workshop (Oral).
[02/2025] Released InterFeedback—exploring whether LMMs evolve via interactive human feedback.
[02/2025] New preprint: WorldGUI: Dynamic Testing for Comprehensive Desktop GUI Automation.
[12/2024] Presenting LOVA3 at NeurIPS (Vancouver).
[10/2024] Presenting Genixer at ECCV (Milan).
[09/2024] One paper accepted to NeurIPS 2024.
[07/2024] One paper accepted to ECCV 2024.
[09/2023] One paper accepted to IJCV 2023.
[08/2023] One paper accepted to TPAMI 2023.

🌺 Research Papers

Multimodal Model Alignment:
	InterFeedback: Unveiling Interactive Intelligence of Large Multimodal Models via Human Feedback Henry Hengyuan Zhao, Wenqi Pei, Yifei Tao, Haiyang Mei, Mike Zheng Shou Equal contribution EMNLP 2025 Findings ICLR 2025 @ Bi-Align Workshop (Oral) We study whether LMMs can evolve via interactive human feedback and find: (1) Accuracy may not fully capture intelligence; (2) LMMs may cater to humans; (3) Low-quality feedback can hurt more than simple binary feedback. Paper \| [新智元] \| Dataset \| AK's Retweet
Human-Agent-Computer Interaction:
	WorldGUI: An Interactive Benchmark for Desktop GUI Automation from Any Starting Point Henry Hengyuan Zhao, Kaiming Yang, Wendi Yu, Difei Gao, Mike Zheng Shou ACL 2025 @ REALM Workshop The first GUI benchmark targeting dynamic planning and action-event detection for desktop automation. Paper \| Codes \| Project Page
The Roles of MLLMs:
	LOVA3: Learning to Visual Question Answering, Asking and Assessment Henry Hengyuan Zhao, Pan Zhou, Difei Gao, Zechen Bai, Mike Zheng Shou NeurIPS 2024 Beyond answering: we introduce asking and assessing during training and obtain consistent gains without extra annotation or hyper-parameter tuning. Paper \| Codes
	Genixer: Empowering Multimodal Large Language Models as a Powerful Data Generator Henry Hengyuan Zhao, Pan Zhou, Mike Zheng Shou ECCV 2024 First to examine MLLMs for data generation—showing diverse data synthesis and measurable downstream gains. Paper \| Codes
Parameter-Efficient Tuning:
	SCT: A Simple Baseline for Parameter-Efficient Fine-Tuning via Salient Channels Henry Hengyuan Zhao, Pichao Wang, Yuyang Zhao, Hao Luo, Fan Wang, Mike Zheng Shou IJCV 2023 Tuning only a small set of salient channels achieves up to 780× parameter reduction vs. full fine-tuning. Paper \| Codes
Low-level Vision:
	Evaluating the Generalization Ability of Super-Resolution Networks Yihao Liu, Hengyuan Zhao, Jinjin Gu, Yu Qiao, Chao Dong TPAMI 2023 Paper \| Codes
	ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic Xiangtao Kong, Hengyuan Zhao, Yu Qiao, Chao Dong CVPR 2021 Paper \| Codes
	Efficient Image Super-Resolution Using Pixel Attention Hengyuan Zhao, Xiangtao Kong, Jingwen He, Yu Qiao, Chao Dong ECCVW 2020 Over 400 citations. Paper \| Codes

Multimodal Model Alignment:
	InterFeedback: Unveiling Interactive Intelligence of Large Multimodal Models via Human Feedback Henry Hengyuan Zhao, Wenqi Pei, Yifei Tao, Haiyang Mei, Mike Zheng Shou Equal contribution EMNLP 2025 Findings ICLR 2025 @ Bi-Align Workshop (Oral) We study whether LMMs can evolve via interactive human feedback and find: (1) Accuracy may not fully capture intelligence; (2) LMMs may cater to humans; (3) Low-quality feedback can hurt more than simple binary feedback. Paper \| [新智元] \| Dataset \| AK's Retweet
Human-Agent-Computer Interaction:
	WorldGUI: An Interactive Benchmark for Desktop GUI Automation from Any Starting Point Henry Hengyuan Zhao, Kaiming Yang, Wendi Yu, Difei Gao, Mike Zheng Shou ACL 2025 @ REALM Workshop The first GUI benchmark targeting dynamic planning and action-event detection for desktop automation. Paper \| Codes \| Project Page
The Roles of MLLMs:
	LOVA3: Learning to Visual Question Answering, Asking and Assessment Henry Hengyuan Zhao, Pan Zhou, Difei Gao, Zechen Bai, Mike Zheng Shou NeurIPS 2024 Beyond answering: we introduce asking and assessing during training and obtain consistent gains without extra annotation or hyper-parameter tuning. Paper \| Codes
	Genixer: Empowering Multimodal Large Language Models as a Powerful Data Generator Henry Hengyuan Zhao, Pan Zhou, Mike Zheng Shou ECCV 2024 First to examine MLLMs for data generation—showing diverse data synthesis and measurable downstream gains. Paper \| Codes
Parameter-Efficient Tuning:
	SCT: A Simple Baseline for Parameter-Efficient Fine-Tuning via Salient Channels Henry Hengyuan Zhao, Pichao Wang, Yuyang Zhao, Hao Luo, Fan Wang, Mike Zheng Shou IJCV 2023 Tuning only a small set of salient channels achieves up to 780× parameter reduction vs. full fine-tuning. Paper \| Codes
Low-level Vision:
	Evaluating the Generalization Ability of Super-Resolution Networks Yihao Liu, Hengyuan Zhao, Jinjin Gu, Yu Qiao, Chao Dong TPAMI 2023 Paper \| Codes
	ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic Xiangtao Kong, Hengyuan Zhao, Yu Qiao, Chao Dong CVPR 2021 Paper \| Codes
	Efficient Image Super-Resolution Using Pixel Attention Hengyuan Zhao, Xiangtao Kong, Jingwen He, Yu Qiao, Chao Dong ECCVW 2020 Over 400 citations. Paper \| Codes