Vision-language models such as CLIP learn a generic text-image embedding from large-
scale training data. A vision-language model can be adapted to a new classification task …

Large-scale contrastive vision-language pretraining has shown significant progress in visual
representation learning. Unlike traditional visual systems trained by a fixed set of discrete …

Vision-language models such as CLIP are pretrained on large volumes of internet sourced
image and text pairs, and have been shown to sometimes exhibit impressive zero-and low …

Foundational image-language models have generated considerable interest due to their
efficient adaptation to downstream tasks by prompt learning. Prompt learning treats part of …

Contrastive vision-language models like CLIP have shown great progress in transfer
learning. In the inference stage, the proper text description, also known as prompt, needs to …

Recent advances in pre-training vision-language models like CLIP have shown great
potential in learning transferable visual representations. Nonetheless, for downstream …

Machine learning models have been shown to inherit biases from their training datasets.
This can be particularly problematic for vision-language foundation models trained on …

Abstract Large-scale Vision-Language Models, such as CLIP, learn powerful image-text
representations that have found numerous applications, from zero-shot classification to text …

Prompt engineering is a powerful tool used to enhance the performance of pre-trained
models on downstream tasks. For example, providing the prompt" Let's think step by step" …

With the rise of powerful pre-trained vision-language models like CLIP, it becomes essential
to investigate ways to adapt these models to downstream datasets. A recently proposed …