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Found 4 papers, 2 papers with code
Revisiting the Adversarial Robustness of Vision Language Models: a Multimodal Perspective
Pretrained vision-language models (VLMs) like CLIP have shown impressive generalization performance across various downstream tasks, yet they remain vulnerable to adversarial attacks.
Soft Prompt Generation for Domain Generalization
To further adapt VLMs to downstream tasks, soft prompt is proposed to replace manually designed prompt, which acts as a learning vector that undergoes fine-tuning based on specific domain data.
Improving Cross-domain Few-shot Classification with Multilayer Perceptron
Multilayer perceptron (MLP) has shown its capability to learn transferable representations in various downstream tasks, such as unsupervised image classification and supervised concept generalization.
Prompt-based Distribution Alignment for Unsupervised Domain Adaptation
Therefore, in this paper, we first experimentally demonstrate that the unsupervised-trained VLMs can significantly reduce the distribution discrepancy between source and target domains, thereby improving the performance of UDA.
