## 1 Introduction  
There is a close relationship between human activities and sarcasm communication, which is vital in daily work. Sarcasm is a unique expression, and the Oxford Dictionary defines sarcasm as \"a way of using words that are the opposite of what you mean in order to be unpleasant to someone or to make fun of them\". Given a sample consisting of a sentence \(W\) and image \(I\), the multimodal sarcasm detection aims to predict the sarcasm label of the sample from the label set: \(\{Sarcasm,Non-Sarcasm\}\). From the Internet of Things perspective, identifying sarcastic information can be helpful for machines in many ways, such as making more anthropomorphic human-computer interaction applications on social IoT. It is also the reason why sarcasm detection has attracted people's attention. Social Internet of Things is a \"social network of intelligent objects\" paradigm based on social relationships between objects. It can capture the Internet of Things and discover services and resources through social relationships [1; 2]. Sarcasm detection can be applied in social Internet of Things applications, such as sentiment analysis, opinion mining, and dialogue generation, et al. [3; 4; 5; 6; 7]. Sarcasm may mislead the predictions of sentiment analysis and opinion-mining methods. Sentiment analysis and sarcastic expression can influence the dialogue system's performance in social robots. Another common application is multimodal data analysis on mobile social platforms such as WeChat, Facebook, and Twitter. Early sarcasm detection research mainly utilized machine learning methods to mine information, such as viewpoint or emotional orientation in text for text classification [8; 9]. With the advancements in mobile social media technology, people can share their daily lives or comments on current hot events through multimodal data composed of videos, images, and text issued on platforms like Twitter and Weibo, et al., resulting in a substantial increase in the volume of multimodal data available. Sarcasm detection gradually shifts from unimodal to multimodal research [10]. This paper focuses on multimodal sarcasm detection in images and text on Twitter. When it comes to the sarcasm detection of images and text, we need to recognize that images and text express different psychological states of users. Therefore, multimodal sarcasm detection requires simultaneous extraction of semantic information between images and texts and consideration of their incongruity. For multimodal sarcasm detection composed of images and text, some researchers use concatenation operations [11], attention mechanisms, or graph neural networks to fuse multimodal data [12; 13]. Although these studies have achieved excellent performance, there are still the following drawbacks:  
1. They overlook commonsense knowledge's role in supplementing the information of graphic and textual features. For example, subtitles and background descriptions of images can be used as visual commonsense knowledge to enhance unimodal feature representations. Intuitively, visual commonsense knowledge provides additional semantic information. 2. They lack effective modal interaction approaches. The current research on multimodal interaction is overly simple and needs to view multimodal sarcasm detection tasks from multiple perspectives. 3. They do nottake into account the impact of the model size on mobile devices. Especially when processing multimodal data, it is necessary to extract each modality's features and typically requires a larger modal fusion model. However, most existing methods for sarcasm detection extract semantic representations for each modality separately. Subsequently, they fuse multimodal features through complex networks. The sarcastic information embedded in both text and images mutually complements one another, allowing the expression of the user's current intentions and feelings. In previous literature, each modal feature is modeled separately, and relatively little attention is paid to the interaction between modalities.