In this episode we discuss Decoupled Multimodal Distilling for Emotion Recognition by Authors: Yong Li, Yuanzhi Wang, Zhen Cui Affiliation: PCA Lab, Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China. The paper proposes a decoupled multimodal distillation approach for human multimodal emotion recognition (MER). The proposed approach mitigates the issue of multimodal heterogeneities by enhancing the discriminative features of each modality through crossmodal knowledge distillation. A graph distillation unit (GD-Unit) is used for each decoupled part, and the GD paradigm provides a flexible knowledge transfer manner where the distillation weights can be automatically learned, enabling diverse crossmodal knowledge transfer patterns. The experimental results show that the proposed approach consistently outperforms state-of-the-art MER methods, and the visualization results exhibit meaningful distributional patterns w.r.t. the modality-irrelevant/-exclusive feature spaces.
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