The exponential growth of multimodal data has facilitated significant advances in multimodal classification techniques. Most multimodal classification algorithms aggregate information from different modalities into a unified representation to capture the holistic information. However, existing fusion mechanisms overly rely on high-quality data, which compromises the robustness of the model. Meanwhile, the fusion process can cause the loss of modality-specific information due to inherent modality differences. In this work, we propose a trustworthy cyclic progressive fusion method for multimodal classification, named TCPFMC. To enhance robustness and reduce reliance on high-quality data, we designed a modality energy score to evaluate the confidence of each modality, quantifying the informativeness of the modalities. In addition, for efficient integration of modality-specific information, we propose a novel cyclic progressive fusion approach that combines modality information at a fine-grained level, enhancing the overall performance of the model. Extensive experiments on six multimodal datasets show that TCPFMC outperforms state-of-the-art methods. The original code is available at https://github.com/aoli-hrbust/TCPFMC.
Li et al. (Thu,) studied this question.