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

Showing 126150 of 458 papers

TitleStatusHype
Transformer-based Self-supervised Multimodal Representation Learning for Wearable Emotion Recognition0
A transformer-based approach to video frame-level prediction in Affective Behaviour Analysis In-the-wild0
Automatically Classifying Emotions based on Text: A Comparative Exploration of Different Datasets0
NUAA-QMUL-AIIT at Memotion 3: Multi-modal Fusion with Squeeze-and-Excitation for Internet Meme Emotion AnalysisCode0
Cluster-based Deep Ensemble Learning for Emotion Classification in Internet Memes0
See Your Heart: Psychological states Interpretation through Visual Creations0
StockEmotions: Discover Investor Emotions for Financial Sentiment Analysis and Multivariate Time SeriesCode1
Emotion Recognition from Microblog Managing Emoticon with Text and Classifying using 1D CNN0
DFME: A New Benchmark for Dynamic Facial Micro-expression Recognition0
Toward cross-subject and cross-session generalization in EEG-based emotion recognition: Systematic review, taxonomy, and methods0
Is Style All You Need? Dependencies Between Emotion and GST-based Speaker RecognitionCode0
MARLIN: Masked Autoencoder for facial video Representation LearnINgCode2
Improved acoustic-to-articulatory inversion using representations from pretrained self-supervised learning modelsCode0
Combining Contrastive and Non-Contrastive Losses for Fine-Tuning Pretrained Models in Speech Analysis0
Experiencer-Specific Emotion and Appraisal Prediction0
Transformer-based Text Classification on Unified Bangla Multi-class Emotion CorpusCode0
Natural Language Inference Prompts for Zero-shot Emotion Classification in Text across CorporaCode1
Machine Learning For Classification Of Antithetical Emotional States0
Classification of eye-state using EEG recordings: speed-up gains using signal epochs and mutual information measure0
A Monotonicity Constrained Attention Module for Emotion Classification with Limited EEG DataCode0
KAM -- a Kernel Attention Module for Emotion Classification with EEG DataCode0
Dilated Context Integrated Network with Cross-Modal Consensus for Temporal Emotion Localization in VideosCode0
Extending RNN-T-based speech recognition systems with emotion and language classification0
ArmanEmo: A Persian Dataset for Text-based Emotion DetectionCode0
BYEL : Bootstrap Your Emotion LatentCode0
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Benchmark Results

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