SOTAVerified

Emotion Recognition

Emotion Recognition is an important area of research to enable effective human-computer interaction. Human emotions can be detected using speech signal, facial expressions, body language, and electroencephalography (EEG). Source: Using Deep Autoencoders for Facial Expression Recognition

Papers

Showing 126150 of 2041 papers

TitleStatusHype
GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective ComputingCode1
Cross Task Neural Architecture Search for EEG Signal ClassificationsCode1
Cross-Lingual Cross-Age Group Adaptation for Low-Resource Elderly Speech Emotion RecognitionCode1
Crowdsourced and Automatic Speech Prominence EstimationCode1
Decoupled Multimodal Distilling for Emotion RecognitionCode1
Contrast and Generation Make BART a Good Dialogue Emotion RecognizerCode1
Cooperative Sentiment Agents for Multimodal Sentiment AnalysisCode1
Contextual Information and Commonsense Based Prompt for Emotion Recognition in ConversationCode1
CoMPM: Context Modeling with Speaker’s Pre-trained Memory Tracking for Emotion Recognition in ConversationCode1
Continuous Emotion Recognition using Visual-audio-linguistic information: A Technical Report for ABAW3Code1
Cross Attentional Audio-Visual Fusion for Dimensional Emotion RecognitionCode1
Deep Multilayer Perceptrons for Dimensional Speech Emotion RecognitionCode1
CLARA: Multilingual Contrastive Learning for Audio Representation AcquisitionCode1
ChatGPT: Jack of all trades, master of noneCode1
Cluster-Level Contrastive Learning for Emotion Recognition in ConversationsCode1
CARAT: Contrastive Feature Reconstruction and Aggregation for Multi-Modal Multi-Label Emotion RecognitionCode1
CAGE: Circumplex Affect Guided Expression InferenceCode1
Compact Graph Architecture for Speech Emotion RecognitionCode1
CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in ConversationCode1
Context Based Emotion Recognition using EMOTIC DatasetCode1
Context De-confounded Emotion RecognitionCode1
CFN-ESA: A Cross-Modal Fusion Network with Emotion-Shift Awareness for Dialogue Emotion RecognitionCode1
Conversation Understanding using Relational Temporal Graph Neural Networks with Auxiliary Cross-Modality InteractionCode1
CMCRD: Cross-Modal Contrastive Representation Distillation for Emotion RecognitionCode1
BiosERC: Integrating Biography Speakers Supported by LLMs for ERC TasksCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1M2D-CLAPEmoA77.4Unverified
2M2D2EmoA76.7Unverified
3M2DEmoA76.1Unverified
4Jukebox (Pre-training: CALM)EmoA72.1Unverified
5CLMR (Pre-training: contrastive)EmoA67.8Unverified
#ModelMetricClaimedVerifiedStatus
1LogisticRegression on posteriors of xlsr-Wav2Vec2.0&bi-LSTM+AttentionAccuracy86.7Unverified
2MultiMAE-DERWAR83.61Unverified
3Intermediate-Attention-FusionAccuracy81.58Unverified
4Logistic Regression on posteriors of the CNN-14&biLSTM-GuidedSTAccuracy80.08Unverified
5ERANN-0-4Accuracy74.8Unverified
#ModelMetricClaimedVerifiedStatus
1CAGETop-3 Accuracy (%)14.73Unverified
2FocusCLIPTop-3 Accuracy (%)13.73Unverified
#ModelMetricClaimedVerifiedStatus
1VGG based5-class test accuracy66.13Unverified
#ModelMetricClaimedVerifiedStatus
1MaSaC-ERC-ZF1-score (Weighted)51.17Unverified
#ModelMetricClaimedVerifiedStatus
1BiHDMAccuracy40.34Unverified
#ModelMetricClaimedVerifiedStatus
1w2v2-L-robust-12Concordance correlation coefficient (CCC)0.64Unverified
#ModelMetricClaimedVerifiedStatus
14D-aNNAccuracy96.1Unverified
#ModelMetricClaimedVerifiedStatus
1CNN1'"1Unverified