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

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Showing 451–475 of 2041 papers

TitleStatusHype
Robust EEG-based Emotion Recognition Using an Inception and Two-sided Perturbation Model—0
MFHCA: Enhancing Speech Emotion Recognition Via Multi-Spatial Fusion and Hierarchical Cooperative Attention—0
Multi-channel Emotion Analysis for Consensus Reaching in Group Movie Recommendation Systems—0
Authentic Emotion Mapping: Benchmarking Facial Expressions in Real NewsCode0
Cooperative Sentiment Agents for Multimodal Sentiment AnalysisCode1
TRNet: Two-level Refinement Network leveraging Speech Enhancement for Noise Robust Speech Emotion Recognition—0
Alleviating Catastrophic Forgetting in Facial Expression Recognition with Emotion-Centered Models—0
Dynamic Modality and View Selection for Multimodal Emotion Recognition with Missing Modalities—0
Context-Aware Siamese Networks for Efficient Emotion Recognition in Conversation—0
Deep CNN with late fusion for realtime multimodal emotion recognition—0
Joint Contrastive Learning with Feature Alignment for Cross-Corpus EEG-based Emotion Recognition—0
Customising General Large Language Models for Specialised Emotion Recognition TasksCode0
MMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression Recognition in-the-wildCode2
Improving Personalisation in Valence and Arousal Prediction using Data Augmentation—0
AIMDiT: Modality Augmentation and Interaction via Multimodal Dimension Transformation for Emotion Recognition in Conversations—0
The Power of Properties: Uncovering the Influential Factors in Emotion Classification—0
Resolve Domain Conflicts for Generalizable Remote Physiological MeasurementCode1
Multimodal Emotion Recognition by Fusing Video Semantic in MOOC Learning Scenarios—0
What is Learnt by the LEArnable Front-end (LEAF)? Adapting Per-Channel Energy Normalisation (PCEN) to Noisy ConditionsCode0
nEMO: Dataset of Emotional Speech in PolishCode0
Improving Facial Landmark Detection Accuracy and Efficiency with Knowledge Distillation—0
Dynamic Resolution Guidance for Facial Expression Recognition—0
Facial Affective Behavior Analysis with Instruction TuningCode1
Towards Bi-Hemispheric Emotion Mapping through EEG: A Dual-Stream Neural Network Approach—0
Music Recommendation Based on Facial Emotion Recognition—0
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All datasetsEmomusicRAVDESSEMOTICFER2013MaSaC_ERCMPEDMSP-PodcastSEEDคลิปคุณพ่อให้ลูกสาวยืมโทรศัพท์และความสนุกสนาน

Benchmark Results

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