## 1 Introduction
Therefore, they propose an incongruuity-aware attention network (IWAN) to detect sarcasm through a scoring mechanism for word-level incongruuities between modalities. [PERSON] et al. [11] believe that contextual and multimodal information is sometimes unhelpful for sarcasm detection,  
Figure 1: Inconsistency in sarcasm. The text and tone in Figure (**a**) show the same feelings, while the expression shows the opposite. In daily life, a low style usually expresses boredom, while a surprised expression expresses exaggeration and wonder. The feelings expressed by the two are inconsistent, which is a kind of sarcasm. In Figure (**b**), the character’s mood changes from happiness to sadness, indicating an inconsistency of feelings, so this is another expression of sarcasm. so they extended the dataset proposed by [PERSON] et al. [9] using emojis and offered an emoj-aware multitask deep learning framework. However, such methods have a disadvantage in that the training speed is slow, and it ignores essential information, namely, that there is a large amount of data irrelevant to sarcasm detection in the video modality, such as the background information in the video. In this paper, we propose an efficient feature adaptive fusion network with a facial feature for multimodal sarcasm detection (EFAFN). We utilize three types of components, namely, text, speech, and face image features, and use an adaptive feature fusion strategy to fuse the three types of features into a single vector for prediction. Our model is divided into three stages: multimodal feature extraction, adaptive feature fusion, and feature classification. First, use BERT [12] and Librosa [13] to extract the features of text and speech modalities, and then use the face detection tool provided by DLIB to cut out all the face images in each frame for horizontal stitching operation. The obtained stitched image is used as the input of ResNet-152 [14] to obtain the face image features of each video. Second, three types of parts are fused using an adaptive fusion strategy, which is different from other fusion strategies because it uses a fusion weight parameter to control the incongruity of information between different modalities. Furthermore, high performance is another manifestation of the fusion strategy. Finally, the fused vector is sent to the fully connected layer for prediction. Our results show that image features from face regions are more helpful for model performance. Furthermore, our fusion strategy is more effective than simply concatenating the three types of components. To sum up, the adaptive fusion network with facial features is effective for sarcasm detection and it has the advantages of fast fusion speed and high performance. In addition, the model proposed in this paper solves the existence of information irrelevant to sarcasm detection in video modalities, improves the efficiency of multimodal fusion strategies, and promotes the practical implementation requirements of multimodal sarcasm detection. It has significant reference significance for other tasks in multimodal fields. The rest of this paper is organized as follows: Section 2 mainly introduces the related research work. Section 3 presents the architecture of our proposed efficient adaptive fusion network with facial features and the feature fusion strategy. Section 4 offers our proposed data augmentation method, baseline model, experimental setup, and evaluation metrics. Section 5 presents extensive experimental results and demonstrates the effectiveness of our network. Section 6 presents the conclusions of this paper and ideas for future research work. Our main contributions are summarized as follows:  
* We propose a new multimodal deep learning sarcasm detection model, which aims to solve the problem that the existing multimodal sarcasm detection models based on three modalities of text, speech, and image only take the whole image as input, which will bring the model a sea of redundant image data, thus affecting the classification accuracy of the model.