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Multi-label image classification is a prediction task that aims to identifymore than one label from a given image. This paper considers the semanticconsistency of the latent space between the visual patch and linguistic labeldomains and introduces the conditional transport (CT) theory to bridge theacknowledged gap. While recent cross-modal attention-based studies haveattempted to align such two representations and achieved impressiveperformance, they required carefully-designed alignment modules and extracomplex operations in the attention computation. We find that by formulatingthe multi-label classification as a CT problem, we can exploit the interactionsbetween the image and label efficiently by minimizing the bidirectional CTcost. Specifically, after feeding the images and textual labels into themodality-specific encoders, we view each image as a mixture of patch embeddingsand a mixture of label embeddings, which capture the local region features andthe class prototypes, respectively. CT is then employed to learn and alignthose two semantic sets by defining the forward and backward navigators.Importantly, the defined navigators in CT distance model the similaritiesbetween patches and labels, which provides an interpretable tool to visualizethe learned prototypes. Extensive experiments on three public image benchmarksshow that the proposed model consistently outperforms the previous methods.

PatchCT: Aligning Patch Set and Label Set with Conditional Transport for Multi-Label Image Classification