This paper is on soft prompt learning for Vision \& Language (V&L) models.
Similarly to their NLP counterparts, V\&L models can be adapted to a downstream
task by learning soft continuous prompts using a few training examples. Current
methods learn the soft prompts by minimizing a cross-entropy loss using as
class weights the features obtained by passing the prompts plus the class names
through the text encoder. Such methods, however, significantly overfit the
training data suffering from large accuracy degradation when tested on unseen
classes from the same domain. Our main contribution, in this paper, is a
surprisingly simple approach to alleviate this problem: we use a second cross
entropy loss to minimize the distance between the learned soft prompts and a
set of hand-engineered manual prompts (obtained by prompt engineering). The
proposed loss can be interpreted in multiple ways including as a regularizer,
as a means for language-based augmentation, and as a way of learning more
discriminative class centroids. Importantly, our formulation is inherently
amenable to including, during training, virtual classes, i.e. class names for
which no visual samples are available, further increasing the robustness of the
learned prompts. Through extensive evaluations on 11 datasets, we show that our
approach (a) significantly outperforms all prior works on soft prompting, and
(b) matches and surpasses, for the first time, the accuracy on novel classes
obtained by hand-crafted prompts and CLIP for the majority of the test
datasets. Code will be made available.

本文介绍了一种软提示学习的方法，用于 Vision & Language 模型。该方法通过使用第二交叉熵损失来最小化学习的软提示与手工工程提示之间的距离，进而提高模型性能，并能够训练虚拟类。在 11 个数据集上进行的广泛评估表明，该方法显著优于所有先前的软提示工作，并在大多数测试数据集上匹配和超越手工制作提示和 CLIP 的新类准确性。