ICCV 2023 – PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization

In this episode we discuss PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization
by Junhyeong Cho, Gilhyun Nam, Sungyeon Kim, Hunmin Yang, Suha Kwak. The paper introduces a method called PromptStyler for domain generalization in a joint vision-language space. It achieves this by synthesizing diverse styles using prompts without using any images. The method learns to generate different style features using learnable style word vectors and ensures that content information is preserved by keeping style-content features close to their corresponding content features. The results show that PromptStyler outperforms existing methods on multiple benchmark datasets while requiring no images and only a short training time.