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This work investigates the intersection of cross modal learning and semisupervised learning, where we aim to improve the supervised learningperformance of the primary modality by borrowing missing information from anunlabeled modality. We investigate this problem from a Nadaraya Watson (NW)kernel regression perspective and show that this formulation implicitly leadsto a kernelized cross attention module. To this end, we propose The AttentionPatch (TAP), a simple neural network plugin that allows data level knowledgetransfer from the unlabeled modality. We provide numerical simulations on threereal world datasets to examine each aspect of TAP and show that a TAPintegration in a neural network can improve generalization performance usingthe unlabeled modality.

TAP: The Attention Patch for Cross-Modal Knowledge Transfer from Unlabeled Data