16 citations · 46 across the 12 of their papers we have counts for
12 papers
MedVision: Benchmarking Quantitative Medical Image Analysis
Yongcheng Yao, Yongshuo Zong, Raman Dutt +3
Current vision-language models (VLMs) in medicine are primarily designed for categorical question answering (e.g., "Is this normal or abnormal?") or qualitative descriptive tasks.…
Ground-V: Teaching VLMs to Ground Complex Instructions in Pixels
Yongshuo Zong, Qin Zhang, Dongsheng An +6
This work presents a simple yet effective workflow for automatically scaling instruction-following data to elicit pixel-level grounding capabilities of VLMs under complex instructi…
A Preliminary Study of o1 in Medicine: Are We Closer to an AI Doctor?
Yunfei Xie, Juncheng Wu, Haoqin Tu +6
Large language models (LLMs) have exhibited remarkable capabilities across various domains and tasks, pushing the boundaries of our knowledge in learning and cognition. The latest…
Benchmarking Multi-Image Understanding in Vision and Language Models: Perception, Knowledge, Reasoning, and Multi-Hop Reasoning
Bingchen Zhao, Yongshuo Zong, Letian Zhang +1
The advancement of large language models (LLMs) has significantly broadened the scope of applications in natural language processing, with multi-modal LLMs extending these capabili…
Memorized Images in Diffusion Models share a Subspace that can be Located and Deleted
Ruchika Chavhan, Ondrej Bohdal, Yongshuo Zong +2
Large-scale text-to-image diffusion models excel in generating high-quality images from textual inputs, yet concerns arise as research indicates their tendency to memorize and repl…
VL-ICL Bench: The Devil in the Details of Multimodal In-Context Learning
Yongshuo Zong, Ondrej Bohdal, Timothy Hospedales
Large language models (LLMs) famously exhibit emergent in-context learning (ICL) -- the ability to rapidly adapt to new tasks using few-shot examples provided as a prompt, without…