arXiv:1805.02489
공식 코드 · Papers with Code
Transformer for Emotion Recognition
원문 초록
This paper describes the UMONS solution for the OMG-Emotion Challenge. We explore a context-dependent architecture where the arousal and valence of an utterance are predicted according to its surrounding context (i.e. the preceding and following utterances of the video). We report an improvement when taking into account context for both unimodal and multimodal predictions.
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Long-Short Distance Graph Neural Networks and Improved Curriculum Learning for Emotion Recognition in Conversation
Dynamic Parameter Memory: Temporary LoRA-Enhanced LLM for Long-Sequence Emotion Recognition in Conversation
Exploring Remote Physiological Signal Measurement under Dynamic Lighting Conditions at Night: Dataset, Experiment, and Analysis
Infant Cry Emotion Recognition Using Improved ECAPA-TDNN with Multiscale Feature Fusion and Attention Enhancement
Towards Robust Multimodal Emotion Recognition under Missing Modalities and Distribution Shifts
MEDUSA: A Multimodal Deep Fusion Multi-Stage Training Framework for Speech Emotion Recognition in Naturalistic Conditions
토론
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