Multimodal Sentiment Analysis with Missing Modality: A Knowledge-Transfer Approach

Previous research studies [11, 12] have attempted to address the issue of missing modalities in multimodal sentiment Multimodal sentiment analysis aims to identify the emotions analysis. In particular, Tsai et al. [12] proposed a joint expressed by individuals through visual, language, and generative-discriminative objective to obtain a robust multimodal acoustic cues. However, most of the existing research efforts representation and a surrogate inference model for assume that all modalities are available during both missing modalities. Pham et al. [11] developed a multimodal training and testing, making their algorithms susceptible to translation network with a cyclic translation loss for forward the missing modality scenario. In this paper, we propose a adaptation between source and target modalities. However, novel knowledge-transfer network to translate between different the performances of their approaches degrade when complete modalities to reconstruct the missing audio modalities.