Representation Decomposition for Learning Similarity and Contrastness Across Modalities for Affective Computing

Multi-modal affective computing aims to automatically recognize and interpret human attitudes from diverse data sources such as images and text, thereby enhancing human-computer interaction and emotion understanding. Existing approaches typically rely on unimodal analysis or straightforward fusion of cross-modal information that fail to capture complex and conflicting evidence presented across different modalities. In this paper, we propose a novel LLM-based approach for affective computing that explicitly deconstructs visual and textual representations into shared (modality-invariant) and modality-specific components. Specifically, our approach firstly encodes and aligns input modalities using pre-trained multi-modal encoders, then employs a representation decomposition framework to separate common emotional content from unique cues, and finally integrates these decomposed signals via an attention mechanism to form a dynamic soft prompt for a multi-modal LLM. Extensive experiments on three representative tasks for affective computing, namely, multi-modal aspect-based sentiment analysis, multi-modal emotion analysis, and hateful meme detection, demonstrate the effectiveness of our approach, which consistently outperforms strong baselines and state-of-the-art models.

CatVRNN: Generating Category Texts via Multi-task Learning

Controlling the model to generate texts of different categories is a challenging task that is getting more and more attention. Recently, generative adversarial net (GAN) has shown promising results in category text generation. However, the texts generated by GANs usually suffer from the problems of mode collapse and training instability. To avoid the above problems, we propose a novel model named category-aware variational recurrent neural network (CatVRNN), which is inspired by multi-task learning. In our model, generation and classification are trained simultaneously, aiming at generating texts of different categories. Moreover, the use of multi-task learning can improve the quality of generated texts, when the classification task is appropriate. And we propose a function to initialize the hidden state of CatVRNN to force model to generate texts of a specific category. Experimental results on three datasets demonstrate that our model can do better than several state-of-the-art text generation methods based GAN in the category accuracy and quality of generated texts.