Learning Noise-Robust Joint Representation for Multimodal Emotion Recognition under Incomplete Data Scenarios

• URL: http://arxiv.org/abs/2311.16114v2
• Date: Tue, 7 May 2024 16:30:05 GMT
• Title: Learning Noise-Robust Joint Representation for Multimodal Emotion Recognition under Incomplete Data Scenarios
• Authors: Qi Fan, Haolin Zuo, Rui Liu, Zheng Lian, Guanglai Gao,
• Abstract summary: Multimodal emotion recognition (MER) in practical scenarios is significantly challenged by the presence of missing or incomplete data. Traditional methods have often involved discarding data or substituting data segments with zero vectors to approximate these incompletenesses. We introduce a novel noise-robust MER model that effectively learns robust multimodal joint representations from noisy data.
• Score: 23.43319138048058
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal emotion recognition (MER) in practical scenarios is significantly challenged by the presence of missing or incomplete data across different modalities. To overcome these challenges, researchers have aimed to simulate incomplete conditions during the training phase to enhance the system's overall robustness. Traditional methods have often involved discarding data or substituting data segments with zero vectors to approximate these incompletenesses. However, such approaches neither accurately represent real-world conditions nor adequately address the issue of noisy data availability. For instance, a blurry image cannot be simply replaced with zero vectors, and still retain information. To tackle this issue and develop a more precise MER system, we introduce a novel noise-robust MER model that effectively learns robust multimodal joint representations from noisy data. This approach includes two pivotal components: firstly, a noise scheduler that adjusts the type and level of noise in the data to emulate various realistic incomplete situations. Secondly, a Variational AutoEncoder (VAE)-based module is employed to reconstruct these robust multimodal joint representations from the noisy inputs. Notably, the introduction of the noise scheduler enables the exploration of an entirely new type of incomplete data condition, which is impossible with existing methods. Extensive experimental evaluations on the benchmark datasets IEMOCAP and CMU-MOSEI demonstrate the effectiveness of the noise scheduler and the excellent performance of our proposed model.

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