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We abstract the features (i.e. learned representations) of multi-modal datainto 1) uni-modal features, which can be learned from uni-modal training, and2) paired features, which can only be learned from cross-modal interactions.Multi-modal models are expected to benefit from cross-modal interactions on thebasis of ensuring uni-modal feature learning. However, recent supervisedmulti-modal late-fusion training approaches still suffer from insufficientlearning of uni-modal features on each modality. We prove that this phenomenondoes hurt the model's generalization ability. To this end, we propose to choosea targeted late-fusion learning method for the given supervised multi-modaltask from Uni-Modal Ensemble(UME) and the proposed Uni-Modal Teacher(UMT),according to the distribution of uni-modal and paired features. We demonstratethat, under a simple guiding strategy, we can achieve comparable results toother complex late-fusion or intermediate-fusion methods on various multi-modaldatasets, including VGG-Sound, Kinetics-400, UCF101, and ModelNet40.

On Uni-Modal Feature Learning in Supervised Multi-Modal Learning