Missing modalities remain a major obstacle to the real-world deployment of multimodal learning systems, as incomplete inputs can substantially degrade model performance. Existing methods often suffer from biased imputation under high missing rates and lack uncertainty-aware, differentiated processing. Inspired by three-way decision, a framework for handling uncertainty by adding a deferment option to acceptance and rejection, we propose three-way multimodal learning with severely missing modalities (3WML-SMMs), a novel framework that introduces a three-way decision mechanism into both missing-modality imputation and feature regularization for the first time. Specifically, 3WML-SMM treats variance not merely as a descriptive measure of uncertainty, but as a decision signal for adaptive processing. Based on this idea, the framework incorporates (1) a variance-guided three-way imputation strategy with accept–delay–reject decisions to reduce unreliable reconstruction when only a limited number of complete samples are available and (2) a dimension-wise adaptive feature enhancement module that performs fine-grained regularization according to perturbation uncertainty. Experiments on the CMU Multimodal Opinion Sentiment Intensity (CMU-MOSI) and Multimodal Internet Movie Database (MM-IMDb) datasets show that 3WML-SMM consistently outperforms representative baselines, including reconstruction-based methods, complete-input multimodal methods, and missing-modality-specific methods under severe missing-modality settings, with statistically significant improvements over the multimodal learning with severely missing modality (SMIL) baseline (p<0.05). These results demonstrate the effectiveness of the proposed framework, even in extreme settings where only 10% of the text modality is available.
Wang et al. (Sun,) studied this question.