Hyper-Modality Enhancement for Multimodal Sentiment Analysis with Missing Modalities

Multimodal Sentiment Analysis (MSA) aims to infer human emotions by integrating complementary signals from diverse modalities. However, in real-world scenarios, missing modalities are common due to data corruption, sensor failure, or privacy concerns, which can significantly degrade model performance. To tackle this challenge, we propose Hyper-Modality Enhancement (HME), a novel framework that avoids explicit modality reconstruction by enriching each observed modality with semantically relevant cues retrieved from other samples. This cross-sample enhancement reduces reliance on fully observed data during training, making the method better suited to scenarios with inherently incomplete inputs. In addition, we introduce an uncertainty-aware fusion mechanism that adaptively balances original and enriched representations to improve robustness. Extensive experiments on three public benchmarks show that HME consistently outperforms state-of-the-art methods under various missing modality conditions, demonstrating its practicality in real-world MSA applications.

Authors

• Yan Zhuang State Key Laboratory of Cognitive Intelligence, Hefei, China
• Minhao Liu
• Wei Bai
• Yanru Zhang
• Wei Li
• Jiawen Deng University of Electronic Science and Technology of China
• Fuji Ren University of Electronic Science and Technology of China

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