Exploiting modality-invariant feature for robust multimodal emotion recognition with missing modalities

• URL: http://arxiv.org/abs/2210.15359v1
• Date: Thu, 27 Oct 2022 12:16:25 GMT
• Title: Exploiting modality-invariant feature for robust multimodal emotion recognition with missing modalities
• Authors: Haolin Zuo, Rui Liu, Jinming Zhao, Guanglai Gao, Haizhou Li
• Abstract summary: We propose to use invariant features for a missing modality imagination network (IF-MMIN) We show that the proposed model outperforms all baselines and invariantly improves the overall emotion recognition performance under uncertain missing-modality conditions.
• Score: 76.08541852988536
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal emotion recognition leverages complementary information across modalities to gain performance. However, we cannot guarantee that the data of all modalities are always present in practice. In the studies to predict the missing data across modalities, the inherent difference between heterogeneous modalities, namely the modality gap, presents a challenge. To address this, we propose to use invariant features for a missing modality imagination network (IF-MMIN) which includes two novel mechanisms: 1) an invariant feature learning strategy that is based on the central moment discrepancy (CMD) distance under the full-modality scenario; 2) an invariant feature based imagination module (IF-IM) to alleviate the modality gap during the missing modalities prediction, thus improving the robustness of multimodal joint representation. Comprehensive experiments on the benchmark dataset IEMOCAP demonstrate that the proposed model outperforms all baselines and invariantly improves the overall emotion recognition performance under uncertain missing-modality conditions. We release the code at: https://github.com/ZhuoYulang/IF-MMIN.

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