# Multi-Task Supervised Alignment Pre-Training for Few-Shot Multimodal Sentiment Analysis
## 2 Related Work  
In this section, we provide a brief overview of the related work of multimodal sentiment analysis, multi-task learning, and supervised pre-training. **Multimodal Sentiment Analysis**. The applications of multimodal sentiment analysis are extensive, such as intelligent interactions, advertising marketing, and social media analysis [15]. [PERSON]. [16] employed attention mechanisms and Recurrent Neural Networks (RNN) for sentiment analysis and mood detection. The attention mechanism can help the neural network focus on important parts of the sequence, thereby improving the performance of sentiment analysis and sentiment detection. [PERSON]. [17] considered the necessity of uniformly processing text, audio, and image data; employed a deep Long Short-Term Memory (LSTM) model; and combined audio and visual modes for multimodal sentiment analysis. Ref. [18] established a model for information fusion in sentiment analysis tasks. The method takes into account the varying importance of information provided by different modalities for sentiment analysis. Ref. [19] proposed a deep multi-level attention network model for multimodal sentiment analysis. This network uses semantic attention to simulate the relationship between image and text modalities. Despite the sophistication of these methods, most of the above studies rely on large-scale annotated data, which consume a considerable cost. Therefore, we focus on efficiently using a small number of samples to achieve accuracy in sentiment analysis in an environment with limited resources. UP-MPF, as an advanced technology in this field, can well handle the text-image input and only requires pure unlabeled images rather than handcrafted text-image pairs to perform task-aligned multimodal pre-training for few-shot MSA. Based on this method, we use multi-task learning and supervised pre-training to solve the two main problems that UP-MPF currently faces. **Multi-task Learning**. Multi-task learning is an advanced paradigm in machine learning that involves the simultaneous acquisition of knowledge from multiple related tasks. Its fundamental principle is to leverage shared information across tasks, thereby enhancing the model's generalization capabilities and overall performance [20]. Multi-task learning can effectively improve the performance of multimodal sentiment analysis [21]. In multimodal sentiment analysis, multi-task learning optimizes the training process through multiple tasks together, thereby achieving information complementarity between tasks. The different tasks of the model share the same base model, so different tasks can learn shared representations, extracting shared features of various modalities in multimodal sentiment analysis. When the model handles multiple tasks, the likelihood of model overfitting is reduced due to the increase in training data and constraints among multiple tasks. Ref. [22] jointly trains multimodal and unimodal tasks, learning consistency and differences, respectively. In addition, during the training stage, ref. [22] designed a weight adjustment strategy to balance the learning progress between different subtasks. Ref. [23] studied the concept of multi-task learning for multimodal sentiment analysis and explored a contextual multimodal attention framework. Ref. [23] defined three different multi-task settings, and each setting has two tasks. Therefore, we utilized multi-task learning, enabling the model to \"synthesize\" the knowledge learned from multiple tasks and preventing the model from overly focusing on any single task, which solves the second issue of UP-MPF. Specifically, in the classifying features of the MSAP stage, we chose to perform image classification tasks and image style recognition tasks; in addition, we added an image title matching task. These tasks can provide clearer semantic signals, enriching the embedded semantic information. **Supervised Pre-training**.