Multimodal data collected from the real world are often imperfect due to
missing modalities. Therefore multimodal models that are robust against
modal-incomplete data are highly preferred. Recently, Transformer models have
shown great success in processing multimodal data. However, existing work has
been limited to either architecture designs or pre-training strategies; whether
Transformer models are naturally robust against missing-modal data has rarely
been investigated. In this paper, we present the first-of-its-kind work to
comprehensively investigate the behavior of Transformers in the presence of
modal-incomplete data. Unsurprising, we find Transformer models are sensitive
to missing modalities while different modal fusion strategies will
significantly affect the robustness. What surprised us is that the optimal
fusion strategy is dataset dependent even for the same Transformer model; there
does not exist a universal strategy that works in general cases. Based on these
findings, we propose a principle method to improve the robustness of
Transformer models by automatically searching for an optimal fusion strategy
regarding input data. Experimental validations on three benchmarks support the
superior performance of the proposed method.

本研究旨在深入探究 Transformer 模型在缺失模态数据的情况下的行为，并发现最优融合策略是数据集相关的，因此提出一种自动搜索输入数据最优融合策略以提高 Transformer 模型鲁棒性的方法。实验证实了该方法在三个基准数据集上具有优异性能。