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20152025
most citedUnderstanding the Limits of Unsupervised Domain Adaptation via Data Poisoning

5 citations · 13 across the 12 of their papers we have counts for

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cs.CV2025

Visual Variational Autoencoder Prompt Tuning

Xi Xiao, Yunbei Zhang, Yanshuh Li +5

Parameter-efficient fine-tuning (PEFT) has emerged as a crucial approach for adapting large vision transformers to downstream tasks without the prohibitive computational costs of f…

cs.CV2024

OT-VP: Optimal Transport-guided Visual Prompting for Test-Time Adaptation

Yunbei Zhang, Akshay Mehra, Jihun Hamm

Vision Transformers (ViTs) have demonstrated remarkable capabilities in learning representations, but their performance is compromised when applied to unseen domains. Previous meth…

cs.CV2023

On the Fly Neural Style Smoothing for Risk-Averse Domain Generalization

Akshay Mehra, Yunbei Zhang, Bhavya Kailkhura +1

Achieving high accuracy on data from domains unseen during training is a fundamental challenge in domain generalization (DG). While state-of-the-art DG classifiers have demonstrate…

cs.CV2023

Achieving Reliable and Fair Skin Lesion Diagnosis via Unsupervised Domain Adaptation

Janet Wang, Yunbei Zhang, Zhengming Ding +1

The development of reliable and fair diagnostic systems is often constrained by the scarcity of labeled data. To address this challenge, our work explores the feasibility of unsupe…

cs.CV2023

FBA-Net: Foreground and Background Aware Contrastive Learning for Semi-Supervised Atrium Segmentation

Yunsung Chung, Chanho Lim, Chao Huang +2

Medical image segmentation of gadolinium enhancement magnetic resonance imaging (GE MRI) is an important task in clinical applications. However, manual annotation is time-consuming…