# Sentiment Interaction Distillation Network for Image Sentiment Analysis
## 1 Introduction  
Psychological research has proved that visual content (such as images and videos) can evoke various sentimental responses in human observers [1]. Therefore, with the potential applications on sentiment image retrieval and opinion mining, understanding the sentiment of a given image is of great significance [2; 3; 4]. Sentiment analysis is a challenging task because of the diverse set of objects involved and the complex interactions among them. Some researchers have attempted to explore human visual principles using multimodal information and made progress [5; 6], but single-modal image emotional classification tasks have developed less. As shown in Figure 1, images that express the same sentiment contain entirely different entities. Simultaneously, as shown in Figure 1, images containing similar objects such as \"Teddy bear\" express opposite sentiment due to differences in other objects. This indicates that the interaction among objects plays a vital role in visual sentiment. Previous studies focused on how to extract the sentimental features effectively but neglected the interactions. [PERSON] et al. [7] put forward the \"Affective Regions\", which are components that convey significant sentiments information, and utilized three fusion strategies for the features from the global context and \"Affective Regions\". Alternatively, [PERSON] et al. [8] enhanced the local features of images, employed a saliency detection model, and improved the classification performance by no small margin. Local objects effectively enrich the fine-grained features, but they treat the objects as individuals and may see the forest trees. Meanwhile, to supplement the missing globalcontext in local features, most of the study merges the local branch with a global feature branch by feature concatenation or pool [7]. However, as a high-level abstraction, sentiment contains various objects, and mapping local object features directly to sentiment may generate noise, limiting the performance improvement of the model. To solve these problems, we proposed a Sentiment Interaction Distillation Network with two branches to exploit the sentimental interaction and transformation of objects. An object branch captures sentimental object interaction as relational knowledge. Then, we employ knowledge distillation [9] strategy to merge it with a global context branch. More effective than directly merging, the sentimental relationships provide more sufficient and general information of the feature distribution and make the distilled knowledge guide the global branch with a different architecture from its teacher, which can use the feature extraction ability of convolution neural network to suppress noise in sentimental relationship information. Compared with the previous study, knowledge distillation achieves a smooth regularization on logits, which means the model can learn robust features [10]. In the test step, only the global context branch is used to generate the predicted sentiment. This paper introduces the \"sentimental graph\" to model the object interaction in sentiment space. Specifically, with instance segmentation, we build a graph on the input image, where nodes represent objects and edges describe the sentimental correlations among them. To accurately and appropriately describe the sentimental relationships among objects, we design the adjacency matrices on the base of SentiWordNet [11], which is commonly used in natural language sentiment analysis tasks, and annotation the polarity and strength of words. Then, we employ Graph Convolution Networks to update and aggregate the graph representation, which is used as the distilled knowledge to improve the performance of the context branch. We conduct experiments on five public affective datasets, and our model achieves a better result than the state-of-the-art approaches. Our contributions are as follows:  
* We propose the SID network, which makes comprehensive use of the sentiment interaction among objects rather than directly integrating the visual features.