Image Emotion Distribution Learning with Graph Convolutional Networks

Recently, with the rapid progress of techniques in visual analysis, a lot of attention has been paid to affective computing due to its wide potential applications. Traditional affective analysis mainly focus on single label image emotion classification. But a single image may invoke different emotions for different persons, even for one person. So emotion distribution learning is proposed to capture the underlying emotion distribution for images. Currently, state-of-the-art works model the distribution by deep convolutional networks equipped with distribution specific loss. However, the correlation among different emotions is ignored in these works. Some emotions usually co-appear, while some are hardly invoked at the same time. Properly modeling the correlation is important for image emotion distribution learning. Graph convolutional networks have shown great performance in capturing the underlying relationship in graph, and have been successfully applied in vision problems, such as zero-shot image classification. So, in this paper, we propose to apply graph convolutional networks for emotion distribution learning, termed EmotionGCN, which captures the correlation among emotions. The EmotionGCN can make use of correlation either mined from data, or directly from psychological models, such as Mikels' wheel. Extensive experiments are conducted on the FlickrLDL and TwitterLDL datasets, and the results on seven evaluation metrics demonstrate the superiority of the proposed method.