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 776–800 of 2041 papers

TitleStatusHype
Computationally Efficient Wasserstein Loss for Structured Labels—0
APPReddit: a Corpus of Reddit Posts Annotated for Appraisal—0
Emotion Recognition with Pre-Trained Transformers Using Multimodal Signals—0
Computational Emotion Analysis From Images: Recent Advances and Future Directions—0
Emotion Recognition with Machine Learning Using EEG Signals—0
Emotion Recognition with Incomplete Labels Using Modified Multi-task Learning Technique—0
Compound Expression Recognition via Large Vision-Language Models—0
APEX: Attention on Personality based Emotion ReXgnition Framework—0
A Cross-Corpus Speech Emotion Recognition Method Based on Supervised Contrastive Learning—0
Emotion Recognition with Facial Attention and Objective Activation Functions—0
Emotion Recognition with Spatial Attention and Temporal Softmax Pooling—0
Emotion Recognition With Temporarily Localized 'Emotional Events' in Naturalistic Context—0
Emotions in the Loop: A Survey of Affective Computing for Emotional Support—0
Emotion Recognition with CLIP and Sequential Learning—0
Emotion recognition with 4kresolution database—0
EmotionX-AR: CNN-DCNN autoencoder based Emotion Classifier—0
EmotionX-Area66: Predicting Emotions in Dialogues using Hierarchical Attention Network with Sequence Labeling—0
Emotion Recognition Using Wearables: A Systematic Literature Review Work in progress—0
CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in Conversation—0
Emotiphons: Emotion Markers in Conversational Speech - Comparison across Indian Languages—0
EmotiW 2018: Audio-Video, Student Engagement and Group-Level Affect Prediction—0
Emotion Recognition Using Speaker Cues—0
Emotion Recognition using Machine Learning and ECG signals—0
Emotion Recognition Using Fusion of Audio and Video Features—0
A Peek at Peak Emotion Recognition—0
Show:102550
← PrevPage 32 of 82Next →
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