SOTAVerified

Emotion Classification

Emotion classification, or emotion categorization, is the task of recognising emotions to classify them into the corresponding category. Given an input, classify it as 'neutral or no emotion' or as one, or more, of several given emotions that best represent the mental state of the subject's facial expression, words, and so on. Some example benchmarks include ROCStories, Many Faces of Anger (MFA), and GoEmotions. Models can be evaluated using metrics such as the Concordance Correlation Coefficient (CCC) and the Mean Squared Error (MSE).

Papers

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Showing 201–225 of 458 papers

TitleStatusHype
Topological EEG Nonlinear Dynamics Analysis for Emotion Recognition—0
Towards Building an Open-Domain Dialogue System Incorporated with Internet Memes—0
Estimating the Uncertainty in Emotion Class Labels with Utterance-Specific Dirichlet Priors—0
Deep Learning Neural Networks for Emotion Classification from Text: Enhanced Leaky Rectified Linear Unit Activation and Weighted Loss—0
Spectro Temporal EEG Biomarkers For Binary Emotion Classification—0
A Robust Framework for Deep Learning Approaches to Facial Emotion Recognition and Evaluation—0
Automated Feature Extraction on AsMap for Emotion Classification using EEG—0
Learning to Compose Diversified Prompts for Image Emotion Classification—0
A Pre-trained Audio-Visual Transformer for Emotion Recognition—0
Adaptive Transfer Learning for Multi-Label Emotion Classification—0
Emotion Intensity and its Control for Emotional Voice Conversion—0
Temporal Analysis of Functional Brain Connectivity for EEG-based Emotion Recognition—0
The Many Faces of Anger: A Multicultural Video Dataset of Negative Emotions in the Wild (MFA-Wild)Code0
Representation learning through cross-modal conditional teacher-student training for speech emotion recognition—0
Knowledge-guided Transformer for Joint Theme and Emotion Classification of Chinese Classical Poetry—0
Cross-cultural Emotion Classification: the Effect of Emotional Intensity and Acoustic Features—0
Real-time Emotion and Gender Classification using Ensemble CNN—0
Emotion Classification in German Plays with Transformer-based Language Models Pretrained on Historical and Contemporary LanguageCode0
MEmoBERT: Pre-training Model with Prompt-based Learning for Multimodal Emotion Recognition—0
Multi-task Voice Activated Framework using Self-supervised Learning—0
A Study on Using Transfer Learning to Improve BERT Model for Emotional Classification of Chinese Lyrics—0
Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition—0
Fusion with Hierarchical Graphs for Mulitmodal Emotion Recognition—0
Uncovering the Limits of Text-based Emotion DetectionCode0
Probabilistic Ensembles of Zero- and Few-Shot Learning Models for Emotion Classification—0
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All datasetsCMU-MOSEISemEval 2018 Task 1E-cEWALKMFAROCStoriesArmanEmoCAER-DynamicRAVDESSShortPersianEmo

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MARLIN (ViT-L)Accuracy80.63—Unverified
2MARLIN (ViT-B)Accuracy80.6—Unverified
3MARLIN (ViT-S)Accuracy80.38—Unverified
4ConCluGenAccuracy66.48—Unverified
#ModelMetricClaimedVerifiedStatus
1SpanEmoAccuracy0.6—Unverified
2BERT+DKAccuracy0.59—Unverified
3BERT-GCNAccuracy0.59—Unverified
4Transformer (finetune)Macro-F10.56—Unverified
#ModelMetricClaimedVerifiedStatus
1ProxEmo (ours)Accuracy82.4—Unverified
2STEP [bhattacharya2019step]Accuracy78.24—Unverified
3Baseline (Vanilla LSTM) [Ewalk]Accuracy55.47—Unverified
#ModelMetricClaimedVerifiedStatus
1MLKNNF-F1 score (Comb.)0.34—Unverified
2CC - XGBF-F1 score (Comb.)0.33—Unverified
#ModelMetricClaimedVerifiedStatus
1Semi-supervisionF165.88—Unverified
2NPN + Explanation TrainingF130.29—Unverified
#ModelMetricClaimedVerifiedStatus
1Deep ParsBERTMacro F10.65—Unverified
#ModelMetricClaimedVerifiedStatus
1CAERNetAccuracy77.04—Unverified
#ModelMetricClaimedVerifiedStatus
1ERANN-0-4Top-1 Accuracy74.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Deep ParsBERTMacro F10.71—Unverified