  Treating texts as images, combining prompts with textual labels for prompt
tuning, and leveraging the alignment properties of CLIP have been successfully
applied in zero-shot multi-label image recognition. Nonetheless, relying solely
on textual labels to store visual information is insufficient for representing
the diversity of visual objects. In this paper, we propose reversing the
training process of CLIP and introducing the concept of Pseudo Visual Prompts.
These prompts are initialized for each object category and pre-trained on
large-scale, low-cost sentence data generated by large language models. This
process mines the aligned visual information in CLIP and stores it in
class-specific visual prompts. We then employ contrastive learning to transfer
the stored visual information to the textual labels, enhancing their visual
representation capacity. Additionally, we introduce a dual-adapter module that
simultaneously leverages knowledge from the original CLIP and new learning
knowledge derived from downstream datasets. Benefiting from the pseudo visual
prompts, our method surpasses the state-of-the-art not only on clean annotated
text data but also on pseudo text data generated by large language models.
