  Prompt tuning has been employed as an efficient way to adapt large
vision-language pre-trained models (e.g. CLIP) to various downstream tasks in
data-limited or label-limited settings. Nonetheless, visual data (e.g., images)
is by default prerequisite for learning prompts in existing methods. In this
work, we advocate that the effectiveness of image-text contrastive learning in
aligning the two modalities (for training CLIP) further makes it feasible to
treat texts as images for prompt tuning and introduce TaI prompting. In
contrast to the visual data, text descriptions are easy to collect, and their
class labels can be directly derived. Particularly, we apply TaI prompting to
multi-label image recognition, where sentences in the wild serve as
alternatives to images for prompt tuning. Moreover, with TaI, double-grained
prompt tuning (TaI-DPT) is further presented to extract both coarse-grained and
fine-grained embeddings for enhancing the multi-label recognition performance.
Experimental results show that our proposed TaI-DPT outperforms zero-shot CLIP
by a large margin on multiple benchmarks, e.g., MS-COCO, VOC2007, and NUS-WIDE,
while it can be combined with existing methods of prompting from images to
improve recognition performance further. Code is released at
https://github.com/guozix/TaI-DPT.
