TL;DR
It is shown that benchmark performance alone is insufficient to assess multimodal affective understanding, and that future progress requires more robust, interpretable, context-aware, and generalizable representation learning frameworks for real-world MER.
This survey provides a comprehensive review of state-of-the-art methodologies for emotion recognition and generation across facial, speech, and textual modalities, covering preprocessing techniques, datasets, deep learning architectures, evaluation metrics, and emotion control mechanisms.
Emotion recognition is a key capability in human–robot interaction (HRI), but existing multimodal emotion recognition (MER) methods rely mainly on facial, vocal, and textual cues, which are often unreliable in practical HRI settings. In particular, occlusion and degraded or missing modalities can substantially reduce t...
Visual emotion recognition aims to infer human emotional states from visual cues. Adapting pretrained Vision-Language Models (VLMs) to this task remains challenging. In-Context Learning (ICL) enables a frozen model to condition its predictions on a few labeled demonstrations without task-specific fine-tuning. Here, dem...
Experimental evaluations on two public datasets DEAP, AMIGOS and a private dataset MAN-II demonstrate that CLMER significantly outperforms unimodal and traditional fusion approaches, achieving state-of-the-art performance in emotion classification tasks.
The precise identification of musical emotion holds substantial academic and practical value, with critical applications in music recommendation, therapeutic interventions, and real-time human–computer interaction. Conventional paradigms for music emotion recognition, predominantly based on manual feature engineering a...
Human emotion analysis is central to applications such as healthcare monitoring, human–computer interaction, education, and intelligent decision support systems. Existing emotion recognition approaches largely classify emotions into discrete categories rather than estimating fine-grained intensity levels, and typically...
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