3 citations · 12 across the 9 of their papers we have counts for
9 papers
Split to Merge: Unifying Separated Modalities for Unsupervised Domain Adaptation
Xinyao Li, Yuke Li, Zhekai Du +3
Large vision-language models (VLMs) like CLIP have demonstrated good zero-shot learning performance in the unsupervised domain adaptation task. Yet, most transfer approaches for VL…
Agile Multi-Source-Free Domain Adaptation
Xinyao Li, Jingjing Li, Fengling Li +2
Efficiently utilizing rich knowledge in pretrained models has become a critical topic in the era of large models. This work focuses on adaptively utilizing knowledge from multiple…
Domain-Agnostic Mutual Prompting for Unsupervised Domain Adaptation
Zhekai Du, Xinyao Li, Fengling Li +3
Conventional Unsupervised Domain Adaptation (UDA) strives to minimize distribution discrepancy between domains, which neglects to harness rich semantics from data and struggles to…
Order-preserving Consistency Regularization for Domain Adaptation and Generalization
Mengmeng Jing, Xiantong Zhen, Jingjing Li +1
Deep learning models fail on cross-domain challenges if the model is oversensitive to domain-specific attributes, e.g., lightning, background, camera angle, etc. To alleviate this…
Zero-Shot Learning by Harnessing Adversarial Samples
Zhi Chen, Pengfei Zhang, Jingjing Li +2
Zero-Shot Learning (ZSL) aims to recognize unseen classes by generalizing the knowledge, i.e., visual and semantic relationships, obtained from seen classes, where image augmentati…
Imbalanced Open Set Domain Adaptation via Moving-threshold Estimation and Gradual Alignment
Jinghan Ru, Jun Tian, Zhekai Du +3
Multimedia applications are often associated with cross-domain knowledge transfer, where Unsupervised Domain Adaptation (UDA) can be used to reduce the domain shifts. Open Set Doma…