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Emotion Recognition In Context On Emotic
Métriques
mAP
Résultats
Résultats de performance de divers modèles sur ce benchmark
| Paper Title | ||
|---|---|---|
| A. Xenos et al | 38.52 | VLLMs Provide Better Context for Emotion Understanding Through Common Sense Reasoning |
| EmotiCon (Depth-based) | 35.48 | EmotiCon: Context-Aware Multimodal Emotion Recognition using Frege's Principle |
| EmotionCLIP(linear-probe evaluation) | 32.91 | Learning Emotion Representations from Verbal and Nonverbal Communication |
| EmotiCon (GCN) | 32.03 | EmotiCon: Context-Aware Multimodal Emotion Recognition using Frege's Principle |
| Fusion Model (scene sentiment + body features) | 29.45 | Context Based Emotion Recognition using EMOTIC Dataset |
| Affective Graph (GCN) | 28.42 | - |
| Fusion Model (scene + body features) | 27.70 | Context Based Emotion Recognition using EMOTIC Dataset |
| Fusion Model | 27.38 | Context Based Emotion Recognition using EMOTIC Dataset |
| CAER-Net (Adaptive Fusion) | 20.84 | Context-Aware Emotion Recognition Networks |
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