Multimodal Emotion Recognition (MER) focuses on identifying and interpreting emotions from modality-compound inputs. Closely mirroring human cognitive processes in real-world environments, MER has drawn substantial attention from both academia and industry. Recently, a paradigm shift has been unveiled in MER, from leveraging small-scale, task-specific models to Large Language Models (LLMs). We refer to the latter as the MER-with-LLMs paradigm, which offers unprecedented generality, spurring numerous empirical attempts, even alongside speculation about LLMs' potential to achieve general emotional intelligence. However, with these new opportunities come new challenges, including the scarcity of emotionally annotated data, the affective gap both within and across modalities, and the opacity of affective interpretation. To systematically review existing research and guide future exploration, this paper categorizes prior works according to their focus on addressing these challenges into three directions: Affective Data Augmentation, Multimodal Affective Representation, and Multimodal Affective Reasoning. By thoroughly tracing the development, emerging trends, and remaining issues within each direction, this paper aims to provide a clear academic map of the MER-with-LLMs paradigm and foster its structured advancement.

翻译：多模态情感识别（MER）致力于从多模态复合输入中识别和解读情感。由于其高度模拟真实环境中的人类认知过程，该领域已引起学术界和工业界的广泛关注。近年来，MER领域经历范式转变——从依赖小型任务专用模型转向大语言模型（LLMs）。我们将后者称为"基于LLM的MER"范式，该范式凭借前所未有的泛化能力，催生出大量实证研究，甚至引发关于LLMs实现通用情感智能潜力的理论探讨。然而，这些新机遇也伴随着新挑战：情感标注数据的稀缺性、模态内外的情感鸿沟，以及情感解释的不透明性。为系统梳理现有研究并指导未来探索，本文根据应对这些挑战的研究侧重点，将已有工作归纳为三个方向：情感数据增强、多模态情感表征与多模态情感推理。通过深入追踪各方向的发展脉络、新兴趋势及现存问题，本文旨在勾勒基于LLM的MER范式的清晰学术图谱，推动该领域的有序发展。