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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 451–500 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
Improving Personalisation in Valence and Arousal Prediction using Data Augmentation—0
MMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression Recognition in-the-wildCode2
AIMDiT: Modality Augmentation and Interaction via Multimodal Dimension Transformation for Emotion Recognition in Conversations—0
Resolve Domain Conflicts for Generalizable Remote Physiological MeasurementCode1
The Power of Properties: Uncovering the Influential Factors in Emotion Classification—0
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
Dynamic Resolution Guidance for Facial Expression Recognition—0
Improving Facial Landmark Detection Accuracy and Efficiency with Knowledge Distillation—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
IITK at SemEval-2024 Task 10: Who is the speaker? Improving Emotion Recognition and Flip Reasoning in Conversations via Speaker Embeddings—0
Exploring Emotions in Multi-componential Space using Interactive VR Games—0
Affective-NLI: Towards Accurate and Interpretable Personality Recognition in ConversationCode0
Automated Assessment of Encouragement and Warmth in Classrooms Leveraging Multimodal Emotional Features and ChatGPT—0
Heterogeneity over Homogeneity: Investigating Multilingual Speech Pre-Trained Models for Detecting Audio DeepfakeCode0
MIPS at SemEval-2024 Task 3: Multimodal Emotion-Cause Pair Extraction in Conversations with Multimodal Language ModelsCode1
Targeted aspect-based emotion analysis to detect opportunities and precaution in financial Twitter messages—0
UniMEEC: Towards Unified Multimodal Emotion Recognition and Emotion Cause—0
Inclusive Design Insights from a Preliminary Image-Based Conversational Search Systems Evaluation—0
Emotion-Anchored Contrastive Learning Framework for Emotion Recognition in ConversationCode1
Cross-Attention is Not Always Needed: Dynamic Cross-Attention for Audio-Visual Dimensional Emotion Recognition—0
MMCert: Provable Defense against Adversarial Attacks to Multi-modal ModelsCode0
Thelxinoë: Recognizing Human Emotions Using Pupillometry and Machine Learning—0
Fusion approaches for emotion recognition from speech using acoustic and text-based features—0
Accuracy enhancement method for speech emotion recognition from spectrogram using temporal frequency correlation and positional information learning through knowledge transferCode1
The Strong Pull of Prior Knowledge in Large Language Models and Its Impact on Emotion Recognition—0
Emotion Recognition from the perspective of Activity Recognition—0
Unlocking the Emotional States of High-Risk Suicide Callers through Speech AnalysisCode0
emoDARTS: Joint Optimisation of CNN & Sequential Neural Network Architectures for Superior Speech Emotion RecognitionCode1
The NeurIPS 2023 Machine Learning for Audio Workshop: Affective Audio Benchmarks and Novel Data—0
Recursive Joint Cross-Modal Attention for Multimodal Fusion in Dimensional Emotion RecognitionCode1
Emotion Recognition Using Transformers with Masked LearningCode0
Audio-Visual Compound Expression Recognition Method based on Late Modality Fusion and Rule-based Decision—0
SUN Team's Contribution to ABAW 2024 Competition: Audio-visual Valence-Arousal Estimation and Expression Recognition—0
Boosting Continuous Emotion Recognition with Self-Pretraining using Masked Autoencoders, Temporal Convolutional Networks, and Transformers—0
Show:102550
← PrevPage 10 of 41Next →
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