8 citations · 14 across the 8 of their papers we have counts for
4 papers · 1 filter
Generalizing Vision-Language Models with Dedicated Prompt Guidance
Xinyao Li, Yinjie Min, Hongbo Chen +3
Fine-tuning large pretrained vision-language models (VLMs) has emerged as a prevalent paradigm for downstream adaptation, yet it faces a critical trade-off between domain specifici…
Unified modality separation: A vision-language framework for unsupervised domain adaptation
Xinyao Li, Jingjing Li, Zhekai Du +2
Unsupervised domain adaptation (UDA) enables models trained on a labeled source domain to handle new unlabeled domains. Recently, pre-trained vision-language models (VLMs) have dem…
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…
Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation
Zhekai Du, Jingjing Li, Hongzu Su +2
Unsupervised Domain Adaptation (UDA) aims to generalize the knowledge learned from a well-labeled source domain to an unlabeled target domain. Recently, adversarial domain adaptati…