Multimodal End-to-End Sparse Model for Emotion Recognition

• URL: http://arxiv.org/abs/2103.09666v1
• Date: Wed, 17 Mar 2021 14:05:05 GMT
• Title: Multimodal End-to-End Sparse Model for Emotion Recognition
• Authors: Wenliang Dai, Samuel Cahyawijaya, Zihan Liu, Pascale Fung
• Abstract summary: We develop a fully end-to-end model that connects the two phases and optimize them jointly. We also restructure the current datasets to enable the fully end-to-end training. Experimental results show that our fully end-to-end model significantly surpasses the current state-of-the-art models.
• Score: 40.71488291980002
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing works on multimodal affective computing tasks, such as emotion recognition, generally adopt a two-phase pipeline, first extracting feature representations for each single modality with hand-crafted algorithms and then performing end-to-end learning with the extracted features. However, the extracted features are fixed and cannot be further fine-tuned on different target tasks, and manually finding feature extraction algorithms does not generalize or scale well to different tasks, which can lead to sub-optimal performance. In this paper, we develop a fully end-to-end model that connects the two phases and optimizes them jointly. In addition, we restructure the current datasets to enable the fully end-to-end training. Furthermore, to reduce the computational overhead brought by the end-to-end model, we introduce a sparse cross-modal attention mechanism for the feature extraction. Experimental results show that our fully end-to-end model significantly surpasses the current state-of-the-art models based on the two-phase pipeline. Moreover, by adding the sparse cross-modal attention, our model can maintain performance with around half the computation in the feature extraction part.

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