Yucheng Han

I graduated from Tsinghua University in 2021. Now I am a third year Ph.D. student at Nanyang Technological University advised by Professor Hanwang Zhang. Recently I work as an intern advised by Gang Yu.

I mainly focus on computer vision. I can be contacted using email yucheng002@e.ntu.edu.sg.

I'm on the job market and looking for a Research Scientist/Engineer starting from 2025 summer. Feel free to contact me if you have any opening!

Email / CV / Scholar / Twitter / Github

Research

I'm interested in computer vision, and generative AI recently. I also have some experiences in Multi-modal Large Language Model, 3D object detection, prompt learning, and video summarization. .

	EMMA: Your Text-to-Image Diffusion Model Can Secretly Accept Multi-Modal Prompts Yucheng Han, Rui Wang, Chi Zhang, Juntao Hu, Pei Cheng, Bin Fu, Hanwang Zhang arXiv*, 2024 project page / arXiv EMMA, a novel model based on ELLA, enhances the capability of multi-modal conditioned image generation by a unique perceiver resampler. It maintains fidelity and detail in generated images, and follows text instructions at the same time, proving an effective solution for diverse multi-modal conditional image generation tasks.
	AppAgent: Multimodal Agents as Smartphone Users Chi Zhang, Zhao Yang, Jiaxuan Liu, Yucheng Han, Xin Chen, Zebiao Huang, Bin Fu, Gang Yu arXiv*, 2024 project page / arXiv This paper introduces a novel LLM-based multimodal agent framework designed to operate smartphone applications. The framework enables the agent to operate smartphone applications through a simplified action space, mimicking human-like interactions such as tapping and swiping.
	ChartLlama: A Multimodal LLM for Chart Understanding and Generation Yucheng Han, Chi Zhang, Xin Chen, Xu Yang, Zhibin Wang, Gang Yu, Bin Fu, Hanwang Zhang arXiv, 2024 project page / arXiv This paper propose an instruction-following dataset construction method for chart figures and finetune a LLaVA-1.5-13B to comprehend and generate chart figures.
	Dual-Perspective Knowledge Enrichment for Semi-Supervised 3D Object Detection Yucheng Han, Na Zhao, Weiling Chen, Keng Teck Ma, Hanwang Zhang AAAI*, 2024 project page / arXiv A novel Dual-Perspective Knowledge Enrichment approach named DPKE for semi-supervised 3D object detection. DPKE enriches the knowledge of limited training data, particularly unlabeled data, from data-perspective and feature-perspective.
	Prompt-aligned Gradient for Prompt Tuning Beier Zhu, Yulei Niu, Yucheng Han, Yue Wu, Hanwang Zhang ICCV, 2023 project page / arXiv We present Prompt-aligned Gradient, dubbed ProGrad, to prevent prompt tuning from forgetting the the general knowledge learned from VLMs. In particular, ProGrad only updates the prompt whose gradient is aligned (or non-conflicting) to the "general direction", which is represented as the gradient of the KL loss of the pre-defined prompt prediction.