1 citations · 2 across the 5 of their papers we have counts for
9 papers
Predicting Multi-View Rashomon Representation: Can We Learn Where Models Disagree?
Mingyue Ma, Zongbo Han, Changqing Zhang +1
Foundation models are increasingly adopted across a wide range of applications, often serving as core blocks within AI systems. Yet different foundation models may encode the same…
FAR-DPO: Feasibility-Aware and Robust Direct Preference Optimization for Cyclic Peptide Design
Guofeng Zhang, Rong Han, Xiaoyu Wang +4
Cyclic peptides are emerging as promising molecular scaffolds in drug discovery due to their high binding affinity and structural stability. However, extending generative models fr…
Fusion Anything: A Generalized Multimodal Foundation Model
Huizi Cui, Zongbo Han, Chenggong Ding +6
Making prediction with multimodal data is widely used in diverse scenarios. Existing multimodal fusion models, once deployed, can only handle predefined modalities (e.g., vision, t…
Towards World Models in Biomedical Research
Guangyu Wang, Jingkun Yue, Siqi Zhang +19
A central goal of biomedicine is to understand, predict and ultimately control the dynamic mechanisms by which biological systems respond to perturbations, disease progression and…
Helping CLIP See Both the Forest and the Trees: A Decomposition and Description Approach
Leyan Xue, Zongbo Han, Guangyu Wang +3
Vision-Language Models (VLMs) like CLIP achieve cross-modal semantic alignment through contrastive learning, exhibiting robust zero-shot generalization. Traditional prompt engineer…
Computational Reasoning of Large Language Models
Haitao Wu, Zongbo Han, Joey Tianyi Zhou +2
With the rapid development and widespread application of Large Language Models (LLMs), multidimensional evaluation has become increasingly critical. However, current evaluations ar…
MedSG-Bench: A Benchmark for Medical Image Sequences Grounding
Jingkun Yue, Siqi Zhang, Zinan Jia +4
Visual grounding is essential for precise perception and reasoning in multimodal large language models (MLLMs), especially in medical imaging domains. While existing medical visual…
DOTA: Distributional Test-Time Adaptation of Vision-Language Models
Zongbo Han, Jialong Yang, Guangyu Wang +4
Vision-language foundation models (VLMs), such as CLIP, exhibit remarkable performance across a wide range of tasks. However, deploying these models can be unreliable when signific…
Learning with Noisy Labels over Imbalanced Subpopulations
MingCai Chen, Yu Zhao, Bing He +3
Learning with Noisy Labels (LNL) has attracted significant attention from the research community. Many recent LNL methods rely on the assumption that clean samples tend to have "sm…