## 1 Introduction
Consequently, for sarcasm detection, it is essential to construct a multimodal interaction model that integrates visual commonsense knowledge. For the above reason, this paper focuses on multimodal sarcasm detection that can be used for mobile devices with limited computing resources. We exploit a multi-view interaction model with knowledge enhancement (MVK) for multimodal sarcasm detection in this paper. MVK mainly consists of two parts, interactive learning and feature fusion. Firstly, we employ the pre-trained model ResNet [14] to obtain each image's corresponding attribute labels, which can effectively represent the object and background information of the image. These attribute labels serve as visual commonsense knowledge. From the perspective of multi-view learning, attribute labels can also be used as the image attribute view. Then, we extract images, text, and visual commonsense knowledge representations. In the stage of interaction learning, text and image features are learned and acquired through the self-influence and mutual influence of text and images with the guidance of visual commonsense knowledge. Specifically, we concatenate knowledge features with text and image features, respectively. By employing an attention mechanism, we obtain the importance of knowledge for each modality and enhance the single modality representation. Subsequently, an inter-modal interaction module is established to interact with image and text information to learn inter-modal mutual and incongruuous information. Within this module, the attention weight matrix from the text perspective is employed for extracting image features, whereas the attention weight matrix from the image perspective is employed for extracting text features. We optimize the attention weight matrix for each modality through interactive learning. Finally, in the feature fusion stage, a late-attention mechanism is utilized to fuse the representation vectors of each modality. Additionally, the constructedmultimodal sarcasm detection model has fewer parameters. The experiment demonstrates that the presented approach performs excellently in multimodal sarcasm detection. The main contributions of this paper can be stated as follows:  
* In this paper, we employ ResNet to acquire attribute information of images as visual commonsense knowledge and leverage visual commonsense knowledge to enhance the representation of each modality. * The proposed method enhances the multimodal information interaction. The model can learn the inter-modal differences and mutual information through different modal views and improve the model's robustness. * A series of experiments and analyses indicate that the presented model can effectively utilize the information of text and image to improve the performance of multimodal sarcasm detection. Concurrently, the model we constructed has fewer parameters, reducing memory pressure and computational resources. The rest of the paper is arranged as follows. Section 2 introduces the relevant works. Section 3 provides a detailed description of the method proposed in this paper, and then the experimental setting and results are presented in the Sections 4 and 5. The Section 6 provides a conclusion.