  Recent advances in large pre-trained vision-language models have demonstrated
remarkable performance on zero-shot downstream tasks. Building upon this,
recent studies, such as CoOp and CoCoOp, have proposed the use of prompt
learning, where context within a prompt is replaced with learnable vectors,
leading to significant improvements over manually crafted prompts. However, the
performance improvement for unseen classes is still marginal, and to tackle
this problem, data augmentation has been frequently used in traditional
zero-shot learning techniques. Through our experiments, we have identified
important issues in CoOp and CoCoOp: the context learned through traditional
image augmentation is biased toward seen classes, negatively impacting
generalization to unseen classes. To address this problem, we propose
adversarial token embedding to disentangle low-level visual augmentation
features from high-level class information when inducing bias in learnable
prompts. Through our novel mechanism called "Adding Attributes to Prompt
Learning", AAPL, we guide the learnable context to effectively extract text
features by focusing on high-level features for unseen classes. We have
conducted experiments across 11 datasets, and overall, AAPL shows favorable
performances compared to the existing methods in few-shot learning, zero-shot
learning, cross-dataset, and domain generalization tasks.
