  Recently, large-scale visual language pre-trained (VLP) models have
demonstrated impressive performance across various downstream tasks. Motivated
by these advancements, pioneering efforts have emerged in multi-label image
recognition with missing labels, leveraging VLP prompt-tuning technology.
However, they usually cannot match text and vision features well, due to
complicated semantics gaps and missing labels in a multi-label image. To tackle
this challenge, we propose \textbf{T}ext-\textbf{R}egion \textbf{M}atching for
optimizing \textbf{M}ulti-\textbf{L}abel prompt tuning, namely TRM-ML, a novel
method for enhancing meaningful cross-modal matching. Compared to existing
methods, we advocate exploring the information of category-aware regions rather
than the entire image or pixels, which contributes to bridging the semantic gap
between textual and visual representations in a one-to-one matching manner.
Concurrently, we further introduce multimodal contrastive learning to narrow
the semantic gap between textual and visual modalities and establish
intra-class and inter-class relationships. Additionally, to deal with missing
labels, we propose a multimodal category prototype that leverages intra- and
inter-category semantic relationships to estimate unknown labels, facilitating
pseudo-label generation. Extensive experiments on the MS-COCO, PASCAL VOC,
Visual Genome, NUS-WIDE, and CUB-200-211 benchmark datasets demonstrate that
our proposed framework outperforms the state-of-the-art methods by a
significant margin. Our code is available
here\href{https://github.com/yu-gi-oh-leilei/TRM-ML}{\raisebox{-1pt}{\faGithub}}.
