Self-supervised Multimodal Emotion Recognition Combining Temporal Attention Mechanism and Unimodal Label Automatic Generation Strategy
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摘要: 大多数多模态情感识别方法旨在寻求一种有效的融合机制，构建异构模态的特征，从而学习到具有语义一致性的特征表示。然而，这些方法通常忽略了模态间情感语义的差异性信息。为解决这一问题，提出了一种多任务学习框架，联合训练1个多模态任务和3个单模态任务，分别学习多模态特征间的情感语义一致性信息和各个模态所含情感语义的差异性信息。首先，为了学习情感语义一致性信息，提出了一种基于多层循环神经网络的时间注意力机制(TAM)，通过赋予时间序列特征向量不同的权重来描述情感特征的贡献度。然后，针对多模态融合，在语义空间进行了逐语义维度的细粒度特征融合。其次，为了有效学习各个模态所含情感语义的差异性信息，提出了一种基于模态间特征向量相似度的自监督单模态标签自动生成策略(ULAG)。通过在CMU-MOSI, CMU-MOSEI, CH-SIMS 3个数据集上的大量实验结果证实，提出的TAM-ULAG模型具有很强的竞争力：在分类指标($ Ac{c_2} $, $ {F_1} $)和回归指标(MAE, Corr)上与基准模型的指标相比均有所提升；对于二分类识别准确率，在CMU-MOSI和CMU-MOSEI数据集上分别为87.2%和85.8%，而在CH-SIMS数据集上达到81.47%。这些研究结果表明， 同时学习多模态间的情感语义一致性信息和各模态情感语义的差异性信息，有助于提高自监督多模态情感识别方法的性能。Abstract: Most multimodal emotion recognition methods aim to find an effective fusion mechanism to construct the features from heterogeneous modalities, so as to learn the feature representation with semantic consistency. However, these methods usually ignore the emotionally semantic differences between modalities. To solve this problem, one multi-task learning framework is proposed. By training one multimodal task and three unimodal tasks jointly, the emotionally semantic consistency information among multimodal features and the emotionally semantic difference information contained in each modality are respectively learned. Firstly, in order to learn the emotionally semantic consistency information, one Temporal Attention Mechanism (TAM) based on a multilayer recurrent neural network is proposed. The contribution degree of emotional features is described by assigning different weights to time series feature vectors. Then, for multimodal fusion, the fine-grained feature fusion per semantic dimension is carried out in the semantic space. Secondly, one self-supervised Unimodal Label Automatic Generation (ULAG) strategy based on the inter-modal feature vector similarity is proposed in order to effectively learn the difference information of emotional semantics in each modality. A large number of experimental results on three datasets CMU-MOSI, CMU-MOSEI, CH-SIMS, confirm that the proposed TAM-ULAG model has strong competitiveness, and has improved the classification indices ($ Ac{c_2} $, $ {F_1} $) and regression index (MAE, Corr) compared with the current benchmark models. For binary classification, the recognition rate is 87.2% and 85.8% on the CMU-MOSEI and CMU-MOSEI datasets, and 81.47% on the CH-SIMS dataset. The results show that simultaneously learning the emotionally semantic consistency information and the emotionally semantic difference information for each modality is helpful in improving the performance of self-supervised multimodal emotion recognition method.
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1 自监督单模态标签自动生成
输入：单模态输入$ {I_t} $,$ {I_a} $,$ {I_v} $,多模态标签$ {y_{{mm}}} $ 输出：$ {\mathrm{F}}_k^n $, $ k \in \{ {\text{mm}},{\text{t}},{\text{a}},{\text{v}}\} $, $ n $代表训练次数 1：初始化模型参数$ M\left( {\theta ;x} \right) $ 2：初始化单模态标签$ y_{\text{t}}^1 = {y_{{\text{mm}}}} $, $ y_{\text{a}}^1 = {y_{{\text{mm}}}} $, $ y_{\text{v}}^1 = {y_{{\text{mm}}}} $ 3：初始化全局表征$ {\mathrm{F}}_{\text{t}}^{\text{g}} = 0 $, $ {\mathrm{F}}_{\text{a}}^{\text{g}} = 0 $, $ {\mathrm{F}}_{\text{v}}^{\text{g}} = 0 $, $ {\mathrm{F}}_{{\text{mm}}}^{\text{g}} = 0 $ 4：for 模型训练回合数 do 5： for mini-batch in dataLoader do 6： 通过时间注意力机制计算小批量模态表征$ {{\mathrm{F}}_{\text{t}}} $, $ {{\mathrm{F}}_{\text{a}}} $, $ {{\mathrm{F}}_{\text{v}}} $, $ {{\mathrm{F}}_{{\text{mm}}}} $ 7： 计算多模态表征$ {\mathrm{F}}_{{\text{mm}}}^{\text{*}} $以及预测标签$ \mathop {{y_{{\text{mm}}}}}\limits^ \wedge $ 8： 通过损失函数式(23)来计算损失 9： 计算梯度$ \dfrac{{\partial L}}{{\partial \theta }} $ 10： 更新模型参数$ \theta = \theta - \dfrac{{\partial L}}{{\partial \theta }} $ 11： if $ n \ne 1 $ then 12： 生成相似度分数$ {c_{\text{t}}} $，$ {c_{\text{a}}} $，$ {c_{\text{v}}} $以及通过权重注意力生成单模
态标签$ y_{\text{t}}^n,y_{\text{a}}^n,y_{\text{v}}^n $13： end if 14：利用$ {\mathrm{F}}_s^ * $ $ s \in \left\{ {{\text{mm}},{\text{t}},{\text{a}},{\text{v}}} \right\} $更新全局表征$ {\mathrm{F}}_s^{\text{g}} $ 15： end for 16：end for 
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表 1 数据集的统计信息
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表 2 模型的超参数设置
超参数 CMU-MOSI CMU-MOSEI CMU-SIMS Batch size 32 32 32 Number of Epochs 30 30 30 A-LSTM Dropout Rate 0.0 0.0 0.0 V-LSTM Dropout Rate 0.0 0.0 0.0 BERT Dropout Rate 0.1 0.1 0.1 V-LSTM隐藏层层数 64 32 64 A-LSTM隐藏层层数 32 32 16
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表 3 不同模型对比结果CMU-MOSI数据
模型 Acc2 F1 MAE Corr Acc7 数据设置 TFN [9] 73.90 73.40 0.970 0.633 32.10 非对齐 RAVEN [32] 78.00 76.60 0.915 0.691 33.20 对齐 MCTN [33] 79.36 79.16 0.909 0.676 35.60 对齐 MuIT [34] 81.12 81.08 0.889 0.686 40.00 对齐 MMIM(B)[38] 84.14 84.00 0.700 0.800 46.65 非对齐 MAG-BERT(B)[35] 86.10 86.00 0.712 0.796 / 对齐 MHAI-BER(B)[36] 86.30 86.28 0.727 0.810 / 对齐 ConKI(B)[39] 84.37 84.33 0.681 0.816 48.43 非对齐 MTSA(B)[41] 86.80 86.80 0.696 0.806 46.40 非对齐 MISA(B)[37] 81.80 81.70 0.783 0.761 42.30 对齐 SUGRM(B)$ \otimes $[30] 84.40 84.30 0.703 0.800 / 非对齐 SaPIL(B)$ \otimes $ [42] 83.65 82.51 0.704 0.794 / 非对齐 TETFN(B)$ \otimes $[28] 84.05 83.83 0.717 0.800 / 非对齐 MTL-BAM(B)$ \otimes $[40] 85.36 85.37 0.703 0.798 / 非对齐 TPMSA(B)$ \otimes $[43] 87.00 87.00 0.704 0.799 / 非对齐 HIS-MSA(B)$ \otimes $[29] 86.01 85.99 0.671 0.819 48.40 非对齐 SELF-MM(B)$ \otimes $$ \bullet $[27] 85.02 85.12 0.713 0.798 45.04 非对齐 TAM-ULAG(本文) 87.20 87.12 0.695 0.816 48.94 非对齐
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表 4 不同模型对比结果CMU-MOSEI数据集
模型 Acc2 F1 MAE Corr Acc7 数据设置 TFN [9] 74.30 73.40 0.720 0.497 50.20 非对齐 RAVEN [32] 79.10 79.50 0.614 0.662 / 对齐 MCTN [33] 79.80 80.60 0.609 0.670 / 对齐 MuIT [34] 82.50 82.30 0.580 0.703 48.80 对齐 MMIM(B)[38] 82.50 82.39 0.577 0.716 52.78 非对齐 MAG-BERT(B)[35] 85.10 85.06 0.555 0.758 52.67 对齐 MHAI-BER(B)[36] 85.56 85.52 0.588 0.816 / 对齐 ConKI(B)[39] 82.73 83.08 0.529 0.782 54.25 非对齐 MTSA(B)[41] 82.59 82.67 0.568 0.724 / 非对齐 MISA(B)[37] 83.60 83.80 0.555 0.756 52.20 对齐 SUGRM(B)$ \otimes $[30] 83.70 83.60 0.544 0.748 / 非对齐 SaPIL(B)$ \otimes $ [42] 82.98 83.26 0.523 0.766 / 非对齐 TETFN(B)$ \otimes $[28] 84.25 84.18 0.551 0.748 / 非对齐 MTL-BAM(B)$ \otimes $[40] 84.61 84.71 0.548 0.761 / 非对齐 TPMSA(B)$ \otimes $[43] 85.60 85.60 0.542 0.770 / 非对齐 HIS-MSA(B)$ \otimes $[29] 83.96 83.89 0.510 0.786 54.90 非对齐 SELF-MM(B)$ \otimes $$ \bullet $[27] 82.81 82.53 0.530 0.765 52.80 非对齐 TAM-ULAG(本文) 85.80 86.01 0.518 0.789 54.73 非对齐
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表 6 CMU-MOSI数据集上不同模块的消融实验结果
模型 MAE Corr Acc2 F1 Acc7 w/o BERT 0.713 0.791 84.75 84.81 46.81 w/o 时间注意力 0.704 0.798 85.23 85.25 47.77 w/o 多模态融合 0.701 0.801 84.30 84.21 46.03 w/o ULAG 0.719 0.789 84.72 84.81 47.92 Use ULGM[27] 0.704 0.810 86.98 86.78 48.46 Use SUGRM[30] 0.700 0.811 87.0 86.98 48.53 TAM-ULAG(本文) 0.695 0.816 87.2 87.12 48.94
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表 7 CMU-MOSI数据集上不同模态配置的消融实验结果
模型 MAE Corr Acc2 F1 M 0.732 0.789 83.3 83.3 M, V 0.721 0.791 84.6 84.8 M, A 0.719 0.796 83.8 83.8 M, T 0.704 0.804 85.1 85.3 M, A, V 0.713 0.798 85.5 85.5 M, T, V 0.701 0.800 86.4 86.5 M, T, A 0.708 0.797 86.2 86.1 M, T, A, V 0.695 0.816 87.2 87.12
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表 8 模型在不同数据集上的训练时间、测试时间和参数量的比较
模型 训练时间(ms/单个样本) 测试时间(ms/单个样本) 参数量 SELF-MM[27] CMU-MOSI 4.86 CMU-MOSI 1.57 109 689 220 CMU-MOSEI 5.33 CMU-MOSEI 1.98 CH-SIMS 5.22 CH-SIMS 1.46 MTL-BAM[40] CMU-MOSI 6.33 CMU-MOSI 2.23 112 544 660 CMU-MOSEI 7.18 CMU-MOSEI 2.71 CH-SIMS / CH-SIMS / TAM-ULAG(本文) CMU-MOSI 3.33 CMU-MOSI 1.19 109 694 858 CMU-MOSEI 4.53 CMU-MOSEI 1.43 CH-SIMS 4.21 CH-SIMS 1.23
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表 9 CMU-MOSI数据集上的实例分析
示例 原始标签 文本标签 音频标签 视觉标签
例1
–2.4 –2.47 –2.13 –1.75 例2 
1.8 2.33 –0.55 –0.89 例3 
1.8 2.1 1.2 –1.4
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