  The recent introduction of prompt tuning based on pre-trained vision-language
models has dramatically improved the performance of multi-label image
classification. However, some existing strategies that have been explored still
have drawbacks, i.e., either exploiting massive labeled visual data at a high
cost or using text data only for text prompt tuning and thus failing to learn
the diversity of visual knowledge. Hence, the application scenarios of these
methods are limited. In this paper, we propose a pseudo-visual prompt~(PVP)
module for implicit visual prompt tuning to address this problem. Specifically,
we first learn the pseudo-visual prompt for each category, mining diverse
visual knowledge by the well-aligned space of pre-trained vision-language
models. Then, a co-learning strategy with a dual-adapter module is designed to
transfer visual knowledge from pseudo-visual prompt to text prompt, enhancing
their visual representation abilities. Experimental results on VOC2007,
MS-COCO, and NUSWIDE datasets demonstrate that our method can surpass
state-of-the-art~(SOTA) methods across various settings for multi-label image
classification tasks. The code is available at https://github.com/njustkmg/PVP.
