Sarcasm is a rhetorical technique that expresses meanings opposite to or different from literal meanings through words or behaviors, often used for criticism, satire, humor, or irony, and typically involves mockery or ridicule of certain situations or viewpoints. Due to the complexity of sarcasm, it is difficult to detect it through text alone. Therefore, multi-modal sarcasm detection has received increasing attention from researchers. Existing methods use attention mechanisms for multi-modal sarcasm detection. However, they exhibit limitations in alignment and fusion stages, which ultimately compromises model performance. This study proposes a model based on both attention and graph attention for multi-modal sarcasm detection. This model employs a multi-head cross-modal attention module for alignment and utilizes self-attention to enhance the representation of critical information in the output of both two modules. The effectiveness of this model has been validated on a public dataset for sarcasm detection based on Twitter.