  Multi-label image classification is a prediction task that aims to identify
more than one label from a given image. This paper considers the semantic
consistency of the latent space between the visual patch and linguistic label
domains and introduces the conditional transport (CT) theory to bridge the
acknowledged gap. While recent cross-modal attention-based studies have
attempted to align such two representations and achieved impressive
performance, they required carefully-designed alignment modules and extra
complex operations in the attention computation. We find that by formulating
the multi-label classification as a CT problem, we can exploit the interactions
between the image and label efficiently by minimizing the bidirectional CT
cost. Specifically, after feeding the images and textual labels into the
modality-specific encoders, we view each image as a mixture of patch embeddings
and a mixture of label embeddings, which capture the local region features and
the class prototypes, respectively. CT is then employed to learn and align
those two semantic sets by defining the forward and backward navigators.
Importantly, the defined navigators in CT distance model the similarities
between patches and labels, which provides an interpretable tool to visualize
the learned prototypes. Extensive experiments on three public image benchmarks
show that the proposed model consistently outperforms the previous methods.
