论文标题
不是Levasa！价价结构对齐的潜在编码
It's LeVAsa not LevioSA! Latent Encodings for Valence-Arousal Structure Alignment
论文作者
论文摘要
近年来，情感计算领域已取得了长足的进步。已经开发了几种模型来代表和量化情绪。两个流行的包括（i）分类模型，它们表示为离散标签的情绪，以及（ii）代表Valence-arousal（VA）电路域域中情绪的维度模型。但是，两种标记方法之间没有注释映射的标准。我们构建了一种新颖的算法，用于使用跨情感面部图像数据集的注释转移来映射分类和维模型标签。此外，我们利用转移的注释使用变异自动编码器（VAE）学习丰富且可解释的数据表示。我们提出了“ Levasa”，这是一种VAE模型，通过将潜在空间与VA空间对齐来学习隐式结构。我们通过使用定量和定性分析在两个基准的情感图像数据集上使用定量和定性分析来评估Levasa的功效。我们的结果表明，Levasa实现了高潜在的圆形对准，从而改善了下游的分类情绪预测。这项工作还表明了对齐程度和重建质量之间的权衡。
In recent years, great strides have been made in the field of affective computing. Several models have been developed to represent and quantify emotions. Two popular ones include (i) categorical models which represent emotions as discrete labels, and (ii) dimensional models which represent emotions in a Valence-Arousal (VA) circumplex domain. However, there is no standard for annotation mapping between the two labelling methods. We build a novel algorithm for mapping categorical and dimensional model labels using annotation transfer across affective facial image datasets. Further, we utilize the transferred annotations to learn rich and interpretable data representations using a variational autoencoder (VAE). We present "LeVAsa", a VAE model that learns implicit structure by aligning the latent space with the VA space. We evaluate the efficacy of LeVAsa by comparing performance with the Vanilla VAE using quantitative and qualitative analysis on two benchmark affective image datasets. Our results reveal that LeVAsa achieves high latent-circumplex alignment which leads to improved downstream categorical emotion prediction. The work also demonstrates the trade-off between degree of alignment and quality of reconstructions.
