8 citations · 11 across the 6 of their papers we have counts for
7 papers
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…
LoCA: Location-Aware Cosine Adaptation for Parameter-Efficient Fine-Tuning
Zhekai Du, Yinjie Min, Jingjing Li +5
Low-rank adaptation (LoRA) has become a prevalent method for adapting pre-trained large language models to downstream tasks. However, the simple low-rank decomposition form may con…
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…
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…
Adversarial Energy Disaggregation for Non-intrusive Load Monitoring
Zhekai Du, Jingjing Li, Lei Zhu +2
Energy disaggregation, also known as non-intrusive load monitoring (NILM), challenges the problem of separating the whole-home electricity usage into appliance-specific individual…