most citedeSkinHealth: A Multimodal Dataset for Neglected Tropical Skin Diseases

2 citations · 5 across the 5 of their papers we have counts for

collaborators

9 papers

cs.LG2025

CTR-LoRA: Curvature-Aware and Trust-Region Guided Low-Rank Adaptation for Large Language Models

Zhuxuanzi Wang, Mingqiao Mo, Xi Xiao +6

Parameter-efficient fine-tuning (PEFT) has become the standard approach for adapting large language models under limited compute and memory budgets. Although previous methods impro…

cs.AI20252 cited

eSkinHealth: A Multimodal Dataset for Neglected Tropical Skin Diseases

Janet Wang, Xin Hu, Yunbei Zhang +7

Skin Neglected Tropical Diseases (NTDs) impose severe health and socioeconomic burdens in impoverished tropical communities. Yet, advancements in AI-driven diagnostic support are h…

cs.CV2025

Visual Instance-aware Prompt Tuning

Xi Xiao, Yunbei Zhang, Xingjian Li +5

Visual Prompt Tuning (VPT) has emerged as a parameter-efficient fine-tuning paradigm for vision transformers, with conventional approaches utilizing dataset-level prompts that rema…

cs.CE2025

RoadBench: A Vision-Language Foundation Model and Benchmark for Road Damage Understanding

Xi Xiao, Yunbei Zhang, Janet Wang +9

Accurate road damage detection is crucial for timely infrastructure maintenance and public safety, but existing vision-only datasets and models lack the rich contextual understandi…

cs.CR2025

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation

Yunsung Chung, Yunbei Zhang, Nassir Marrouche +1

Advances in generative models have transformed the field of synthetic image generation for privacy-preserving data synthesis (PPDS). However, the field lacks a comprehensive survey…

cs.CV2025

Doctor Approved: Generating Medically Accurate Skin Disease Images through AI-Expert Feedback

Janet Wang, Yunbei Zhang, Zhengming Ding +1

Paucity of medical data severely limits the generalizability of diagnostic ML models, as the full spectrum of disease variability can not be represented by a small clinical dataset…