The field of affective computing is moving towards leveraging multimodal data, such as audio-visual cues and text-guided fusion, to improve emotion recognition accuracy. Researchers are exploring innovative approaches to reduce the dependence on extensive labeled datasets, including knowledge distillation and zero-shot learning methods. Notably, vision-language models are being used to integrate multiview facial representation learning with semantic guidance from natural language prompts, achieving state-of-the-art accuracy in facial expression recognition. Additionally, compound expression recognition is being addressed through the combination of heterogeneous modalities and dynamic weighting of modality-specific predictions. Noteworthy papers include Leveraging Unlabeled Audio-Visual Data in Speech Emotion Recognition using Knowledge Distillation, which proposes a knowledge distillation framework to transfer knowledge from large teacher models to lightweight student models. Facial Emotion Learning with Text-Guided Multiview Fusion via Vision-Language Model for 3D/4D Facial Expression Recognition is also notable, as it introduces a vision-language framework that achieves state-of-the-art accuracy across multiple benchmarks.