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cs.CV2023
Understanding Prompt Tuning for V-L Models Through the Lens of Neural Collapse
Didi Zhu, Zexi Li, Min Zhang +6
Large-scale vision-language (V-L) models have demonstrated remarkable generalization capabilities for downstream tasks through prompt tuning. However, the mechanisms behind the lea…
cs.CV2023
Generalized Universal Domain Adaptation with Generative Flow Networks
Didi Zhu, Yinchuan Li, Yunfeng Shao +5
We introduce a new problem in unsupervised domain adaptation, termed as Generalized Universal Domain Adaptation (GUDA), which aims to achieve precise prediction of all target label…
cs.CV2023
Universal Domain Adaptation via Compressive Attention Matching
Didi Zhu, Yincuan Li, Junkun Yuan +3
Universal domain adaptation (UniDA) aims to transfer knowledge from the source domain to the target domain without any prior knowledge about the label set. The challenge lies in ho…