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 1351–1400 of 2041 papers

TitleStatusHype
Unifying Geometric Features and Facial Action Units for Improved Performance of Facial Expression Analysis—0
Unifying the Discrete and Continuous Emotion labels for Speech Emotion Recognition—0
UniMEEC: Towards Unified Multimodal Emotion Recognition and Emotion Cause—0
Unimodal-driven Distillation in Multimodal Emotion Recognition with Dynamic Fusion—0
Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion—0
Unsupervised Counselor Dialogue Clustering for Positive Emotion Elicitation in Neural Dialogue System—0
Unsupervised Cross-Lingual Speech Emotion Recognition Using DomainAdversarial Neural Network—0
Unsupervised Discovery of Facial Landmarks and Head Pose—0
Unsupervised Learning in Reservoir Computing for EEG-based Emotion Recognition—0
Unsupervised low-rank representations for speech emotion recognition—0
Unsupervised Multimodal Language Representations using Convolutional Autoencoders—0
Unsupervised Multi-Modal Representation Learning for Affective Computing with Multi-Corpus Wearable Data—0
Unsupervised Personalization of an Emotion Recognition System: The Unique Properties of the Externalization of Valence in Speech—0
Unsupervised Representation Learning with Future Observation Prediction for Speech Emotion Recognition—0
Unsupervised Representations Improve Supervised Learning in Speech Emotion Recognition—0
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
Video Emotion Open-vocabulary Recognition Based on Multimodal Large Language Model—0
Visual and textual prompts for enhancing emotion recognition in video—0
Visual Features for Linguists: Basic image analysis techniques for multimodally-curious NLPers—0
Visually Guided Self Supervised Learning of Speech Representations—0
Visual Prompting in LLMs for Enhancing Emotion Recognition—0
Visual-Texual Emotion Analysis with Deep Coupled Video and Danmu Neural Networks—0
VOCE Corpus: Ecologically Collected Speech Annotated with Physiological and Psychological Stress Assessments—0
VR Based Emotion Recognition Using Deep Multimodal Fusion With Biosignals Across Multiple Anatomical Domains—0
WaDeNet: Wavelet Decomposition based CNN for Speech Processing—0
Watch the Speakers: A Hybrid Continuous Attribution Network for Emotion Recognition in Conversation With Emotion Disentanglement—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