Adaptive Modality Distillation for Separable Multimodal Sentiment Analysis
Wei Peng, Xiaopeng Hong, Guoying Zhao
Abstract
Multimodal sentiment analysis has increasingly attracted attention since with the arrival of complementary data streams, it has great potential to improve and go beyond unimodal sentiment analysis. In this article, we present an efficient separable multimodal learning method to deal with the tasks with modality missing issue. In this method, the multimodal tensor is utilized to guide the evolution of each separated modality representation. To save the computational expense, Tucker decomposition is introduced, which leads to a general extension of the low-rank tensor fusion method with more modality interactions. The method, in turn, enhances our modality distillation processing. Comprehensive experiments on three popular multimodal sentiment analysis datasets, CMU-MOSI, POM, and IEMOCAP, show a superior performance especially when only partial modalities are available.