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 1301–1325 of 2041 papers

TitleStatusHype
Unveiling Emotions from EEG: A GRU-Based Approach—0
Usefulness of Emotional Prosody in Neural Machine Translation—0
Use of Affective Visual Information for Summarization of Human-Centric Videos—0
Use of Variational Inference in Music Emotion Recognition—0
User independent Emotion Recognition with Residual Signal-Image Network—0
User profile-driven large-scale multi-agent learning from demonstration in federated human-robot collaborative environments—0
USI-IR at IEST 2018: Sequence Modeling and Pseudo-Relevance Feedback for Implicit Emotion Detection—0
Using Auxiliary Tasks In Multimodal Fusion Of Wav2vec 2.0 And BERT For Multimodal Emotion Recognition—0
Using Extracted Emotion Cause to Improve Content-Relevance for Empathetic Conversation Generation—0
Using Hankel Matrices for Dynamics-based Facial Emotion Recognition and Pain Detection—0
Using Large Pre-Trained Models with Cross-Modal Attention for Multi-Modal Emotion Recognition—0
Using Photorealistic Face Synthesis and Domain Adaptation to Improve Facial Expression Analysis—0
Using Scene and Semantic Features for Multi-modal Emotion Recognition—0
Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial Representation—0
Using Valence and Arousal-infused Bi-LSTM for Sentiment Analysis in Social Media Product Reviews—0
UTFPR at IEST 2018: Exploring Character-to-Word Composition for Emotion Analysis—0
Utilizing deep learning models for the identification of enhancers and super-enhancers based on genomic and epigenomic features—0
Utilizing Speech Emotion Recognition and Recommender Systems for Negative Emotion Handling in Therapy Chatbots—0
Utterance-Level Multimodal Sentiment Analysis—0
ValueNet: A New Dataset for Human Value Driven Dialogue System—0
Variants of BERT, Random Forests and SVM approach for Multimodal Emotion-Target Sub-challenge—0
Variational Autoencoders for Learning Latent Representations of Speech Emotion: A Preliminary Study—0
Varsini_and_Kirthanna@DravidianLangTech-ACL2022-Emotional Analysis in Tamil—0
Versatile audio-visual learning for emotion recognition—0
Video-Based Frame-Level Facial Analysis of Affective Behavior on Mobile Devices Using EfficientNets—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