Yuanhan (John) Zhang

Hi! I'm Yuanhan Zhang, here is the standard Chinese pronunciation for my first name : Yuanhan, a third-year PhD student at MMLab@NTU, supervised by Prof. Ziwei Liu.

My research interests lie in computer vision and deep learning. In particular, I am focused on adapting foundation models—from vision to multi-modal—for real-world exploration. This involves benchmarking model performance and adapting models through parameter-efficient tuning, in-context learning, instruction tuning, and preference modeling.

Email (yuanhan002@e.ntu.edu.sg)  /  Google Scholar  /  Twitter  /  Github

A powerful open-source large modality model. I worked on video understanding + evaluation.

Fully open-sourced video LMM model with competitive ability, including code, model, and data.

A family of LMMs developed by consolidating insights into data, models, and visual representations.

A vision-language model with in-context instruction tuning.

4 times larger than ImageNet; 2 time larger than Object365; Built by active learning.

Benchmarking the 20 abilities of vision-language models.

An embodied vision-language model trained with RLEF, emerging superior in embodied visual planning and programming.

FunQA benchmarks funny, creative, and magic videos for challenging tasks.

A knowledge augmented vision-language model for AI conservation.

Retrieving prompt for visual in-context learning.

Searching prompt modules for parameter-efficient transfer learning.

3D Point Cloud Pre-training with Knowledge Distillation from 2D Images.

New benchmark for evaluating vision foundation models; New supervised contrastive learning framework.

Large-scale face-antispoofing Dataset.

Last updated in Jun. 2024.

Homepage credits: Jon Barron.