Are Mixture-of-Modality-Experts Transformers Robust to Missing Modality During Training and Inferring?

It is commonly seen that the imperfect multi-modal data with missing modality appears in realistic application scenarios, which usually break the data completeness assumption of multi-modal analysis. Therefore, large efforts in multi-modal learning communities have been made on the robust solution for modality-missing data. Recently, pre-trained models based on Mixture-of-Modality-Experts (MoME) Transformers have been proposed, which achieved competitive performance in various downstream tasks, by utilizing different experts of feed-forward networks for single/multi modal inputs. One natural question arises: are Mixture-of-Modality-Experts Transformers robust to missing modality? To that end, in this paper, we conduct a deep investigation on MoME Transformer under the missing modality problem. Specifically, we propose a novel multi-task learning strategy, which leverages a uniform model to handle missing modalities during training and inference. In this way, the MoME Transformer will be empowered with robustness to missing modality. To validate the effectiveness of our proposed method, we conduct extensive experiments on three popular datasets, which indicate our method could outperform the state-of-the-art (SOTA) methods with a large margin.