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 1751–1800 of 2041 papers

TitleStatusHype
Classifying and Visualizing Emotions with Emotional DANCode0
Embedded Deep Learning for Face Detection and Emotion Recognition with Intel© Movidius (TM) Neural Compute Stick—0
A Multimodal Approach towards Emotion Recognition of Music using Audio and Lyrical Content—0
Multimodal Speech Emotion Recognition Using Audio and TextCode0
DepecheMood++: a Bilingual Emotion Lexicon Built Through Simple Yet Powerful TechniquesCode0
Text-based Sentiment Analysis and Music Emotion Recognition—0
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in ConversationsCode0
USI-IR at IEST 2018: Sequence Modeling and Pseudo-Relevance Feedback for Implicit Emotion Detection—0
CARER: Contextualized Affect Representations for Emotion RecognitionCode0
HGSGNLP at IEST 2018: An Ensemble of Machine Learning and Deep Neural Architectures for Implicit Emotion Classification in Tweets—0
ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection—0
Fine-Grained Emotion Detection in Health-Related Online Posts—0
Leveraging Writing Systems Change for Deep Learning Based Chinese Emotion Analysis—0
BrainT at IEST 2018: Fine-tuning Multiclass Perceptron For Implicit Emotion ClassificationCode0
HUMIR at IEST-2018: Lexicon-Sensitive and Left-Right Context-Sensitive BiLSTM for Implicit Emotion Recognition—0
NL-FIIT at IEST-2018: Emotion Recognition utilizing Neural Networks and Multi-level PreprocessingCode0
Interpretable Emoji Prediction via Label-Wise Attention LSTMs—0
SINAI at IEST 2018: Neural Encoding of Emotional External Knowledge for Emotion Classification—0
Joint Learning for Emotion Classification and Emotion Cause Detection—0
Multi-Modal Sequence Fusion via Recursive Attention for Emotion Recognition—0
UTFPR at IEST 2018: Exploring Character-to-Word Composition for Emotion Analysis—0
Entropy-Assisted Multi-Modal Emotion Recognition Framework Based on Physiological Signals—0
Investigation of Multimodal Features, Classifiers and Fusion Methods for Emotion RecognitionCode0
Convolutional Neural Network Approach for EEG-based Emotion Recognition using Brain Connectivity and its Spatial Information—0
Training Deep Neural Networks with Different Datasets In-the-wild: The Emotion Recognition Paradigm—0
Label-less Learning for Traffic Control in an Edge Network—0
EmotiW 2018: Audio-Video, Student Engagement and Group-Level Affect Prediction—0
Finding Good Representations of Emotions for Text Classification—0
Emotion Recognition in Speech using Cross-Modal Transfer in the Wild—0
Multimodal Local-Global Ranking Fusion for Emotion Recognition—0
Multimodal Language Analysis with Recurrent Multistage FusionCode0
A Survey on Sentiment and Emotion Analysis for Computational Literary Studies—0
An Occam's Razor View on Learning Audiovisual Emotion Recognition with Small Training Sets—0
Contemplating Visual Emotions: Understanding and Overcoming Dataset Bias—0
Normalization Before Shaking Toward Learning Symmetrically Distributed Representation Without Margin in Speech Emotion Recognition—0
Who Feels What and Why? Annotation of a Literature Corpus with Semantic Roles of Emotions—0
Hybrid Attention based Multimodal Network for Spoken Language Classification—0
CAKE: Compact and Accurate K-dimensional representation of Emotion—0
Semi-supervised Deep Generative Modelling of Incomplete Multi-Modality Emotional Data—0
Emotion Recognition from Speech based on Relevant Feature and Majority Voting—0
An Attention Model for group-level emotion recognitionCode0
Construction of a Chinese Corpus for the Analysis of the Emotionality of Metaphorical Expressions—0
Real time P, QRS and T wave detection by QRS matched filter methodCode0
EmotionX-Area66: Predicting Emotions in Dialogues using Hierarchical Attention Network with Sequence Labeling—0
SocialNLP 2018 EmotionX Challenge Overview: Recognizing Emotions in Dialogues—0
Recognizing Emotions in Video Using Multimodal DNN Feature Fusion—0
EmotionX-AR: CNN-DCNN autoencoder based Emotion Classifier—0
Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words—0
Unsupervised Counselor Dialogue Clustering for Positive Emotion Elicitation in Neural Dialogue System—0
Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph—0
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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