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 426–450 of 458 papers

TitleStatusHype
Context-Aware Siamese Networks for Efficient Emotion Recognition in Conversation—0
Contextualized Emotion Recognition in Conversation as Sequence Tagging—0
Contextualized Representations for Low-resource Utterance Tagging—0
Context Unlocks Emotions: Text-based Emotion Classification Dataset Auditing with Large Language Models—0
Continuing Pre-trained Model with Multiple Training Strategies for Emotional Classification—0
Continuous Adversarial Text Representation Learning for Affective Recognition—0
Continuous Metric Learning For Transferable Speech Emotion Recognition and Embedding Across Low-resource Languages—0
Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition—0
Controlling for Confounders in Multimodal Emotion Classification via Adversarial Learning—0
Converting Sentiment Annotated Data to Emotion Annotated Data—0
Convolutional neural networks pretrained on large face recognition datasets for emotion classification from video—0
Corpus Fusion for Emotion Classification—0
Cross-cultural Emotion Classification: the Effect of Emotional Intensity and Acoustic Features—0
Cross-Language Speech Emotion Recognition Using Multimodal Dual Attention Transformers—0
ST-Gait++: Leveraging spatio-temporal convolutions for gait-based emotion recognition on videos—0
Cross-modal Context Fusion and Adaptive Graph Convolutional Network for Multimodal Conversational Emotion Recognition—0
Cross-Task Inconsistency Based Active Learning (CTIAL) for Emotion Recognition—0
Crowdsourcing and Validating Event-focused Emotion Corpora for German and English—0
CrystalFeel at SemEval-2018 Task 1: Understanding and Detecting Emotion Intensity using Affective Lexicons—0
A Robust Framework for Deep Learning Approaches to Facial Emotion Recognition and Evaluation—0
A Contextualized Real-Time Multimodal Emotion Recognition for Conversational Agents using Graph Convolutional Networks in Reinforcement Learning—0
Data Augmentation in Emotion Classification Using Generative Adversarial Networks—0
SwahBERT: Language Model of Swahili—0
Decoding Emotions in Abstract Art: Cognitive Plausibility of CLIP in Recognizing Color-Emotion Associations—0
deepCybErNet at EmoInt-2017: Deep Emotion Intensities in Tweets—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