Multi-Level Confidence Learning for Trustworthy Multimodal Classification - Zheng, Xiao; Tang, Chang; Wan, Zhiguo; Hu, Chengyu; Zhang, Wei (Proceedings of the AAAI Conference on Artificial Intelligence, 2023)
With the rapid development of various data acquisition technologies, more and more multimodal data come into being. It is important to integrate different modalities which are with high-dimensional features for boosting final multimodal data classification task. However, existing multimodal classification methods mainly focus on exploiting the complementary information of different modalities, while ignoring the learning confidence during information fusion. In this paper, we propose a trustworthy multimodal classification network via multi-level confidence learning, referred to as MLCLNet. Considering that a large number of feature dimensions could not contribute to final classification performance but disturb the discriminability of different samples, we propose a feature confidence learning mechanism to suppress some redundant features, as well as enhancing the expression of discriminative feature dimensions in each modality. In order to capture the inherent sample structure information implied in each modality, we design a graph convolutional network branch to learn the corresponding structure preserved feature representation and generate modal-specific initial classification labels. Since samples from different modalities should share consistent labels, a cross-modal label fusion module is deployed to capture the label correlations of different modalities. In addition, motivated the ideally orthogonality of final fused label matrix, we design a label confidence loss to supervise the network for learning more separable data representations. To the best of our knowledge, MLCLNet is the first work which integrates both feature and label-level confidence learning for multimodal classification. Extensive experiments on four multimodal medical datasets are conducted to validate superior performance of MLCLNet when compared to other state-of-the-art methods.