Keywords: Supervised Multi-modal Late-fusion Learning
Abstract: We abstract the features of multi-modal data into 1) uni-modal features, which can be learned from uni-modal training, and 2) paired features, which can only be learned from cross-modal interaction. Multi-modal joint training is expected to benefit from cross-modal interaction on the basis of ensuring uni-modal feature learning. However, recent late-fusion training approaches still suffer from insufficient learning of uni-modal features on each modality and we prove that this phenomenon does hurt the model's generalization ability. Given a multi-modal task, we propose to choose targeted late-fusion learning method from Uni-Modal Ensemble (UME) and the proposed Uni-Modal Teacher (UMT), according to the distribution of uni-modal and paired features. We demonstrate that, under a simple guiding strategy, we can achieve comparable results to other complex late-fusion or intermediate-fusion methods on multi-modal datasets, including VGG-Sound, Kinetics-400, UCF101, and ModelNet40.
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Please Choose The Closest Area That Your Submission Falls Into: Deep Learning and representational learning