11 citations · 12 across the 3 of their papers we have counts for
3 papers
cs.AI2023
EPLKG: Efficient Prompt Learning with Knowledge Graph
YongTaek Lim, Suho Kang, Yewon Kim +2
Large-scale pre-trained models such as CLIP excel in transferability and robust generalization across diverse datasets. However, adapting these models to new datasets or domains is…
cs.CV2023★ 1 cited
BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning
Changdae Oh, Hyeji Hwang, Hee-young Lee +5
With the surge of large-scale pre-trained models (PTMs), fine-tuning these models to numerous downstream tasks becomes a crucial problem. Consequently, parameter efficient transfer…
cs.CV2022★ 11 cited
Geodesic Multi-Modal Mixup for Robust Fine-Tuning
Changdae Oh, Junhyuk So, Hoyoon Byun +4
Pre-trained multi-modal models, such as CLIP, provide transferable embeddings and show promising results in diverse applications. However, the analysis of learned multi-modal embed…