In this paper, we present our solution for the 7th Affective Behavior Analysis and Recognition in-the-Wild (ABAW) competition, which encompasses two sub-challenges: Multi-Task Learning and compound Facial Expression Recognition. The Multi-Task Learning involves the triad of Valence Arousal (VA), Action Units (AU), and facial expressions. The focus of the 7th ABAW competition is on facial expression recognition datasets based on different modalities. In our work, we utilize a plethora of models for visual feature extraction. The Transformer Encoder integrates these features. Additionally, to counteract the potential impact of significant dimensional disparities among various features, we have designed an affine module to align different features to the same dimension. For compound expressions, we propose an ensemble learning-based solution to address this complexity. Our approach involves training two distinct facial expression classification models using convolutional networks and visual transformers. By employing late fusion for model integration, we combine the outputs of these models to predict the final outcomes. Extensive experiments have demonstrated the superiority of our proposed method. For the Multi-Task challenge, we achieved a total score of 1.1776, with an F1 of 0.4997 for AU, a CCC of 0.378 for VA, and an F1 of 0.2997 for EXPR. In the compound Expression sub-challenge, the F1 score was 0.228. The results for both sub-challenges significantly outperform the baselines.