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 1401–1450 of 2041 papers

TitleStatusHype
FERNet: Fine-grained Extraction and Reasoning Network for Emotion Recognition in Dialogues—0
MemoSYS at SemEval-2020 Task 8: Multimodal Emotion Analysis in Memes—0
SemEval-2020 Task 8: Memotion Analysis- the Visuo-Lingual Metaphor!—0
CN-HIT-MI.T at SemEval-2020 Task 8: Memotion Analysis Based on BERT—0
Hitachi at SemEval-2020 Task 8: Simple but Effective Modality Ensemble for Meme Emotion Recognition—0
Summarize before Aggregate: A Global-to-local Heterogeneous Graph Inference Network for Conversational Emotion Recognition—0
An Iterative Emotion Interaction Network for Emotion Recognition in Conversations—0
Towards Label-Agnostic Emotion Embeddings—0
Emotional Semantics-Preserved and Feature-Aligned CycleGAN for Visual Emotion Adaptation—0
Deep Learning in EEG: Advance of the Last Ten-Year Critical Period—0
Self-Supervised learning with cross-modal transformers for emotion recognition—0
Deep Residual Local Feature Learning for Speech Emotion Recognition—0
On the use of Self-supervised Pre-trained Acoustic and Linguistic Features for Continuous Speech Emotion Recognition—0
Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks—0
Interpretable Image Emotion Recognition: A Domain Adaptation Approach Using Facial Expressions—0
Improving Multimodal Accuracy Through Modality Pre-training and Attention—0
WaDeNet: Wavelet Decomposition based CNN for Speech Processing—0
Recognizing More Emotions with Less Data Using Self-supervised Transfer Learning—0
NUAA-QMUL at SemEval-2020 Task 8: Utilizing BERT and DenseNet for Internet Meme Emotion AnalysisCode0
Robust Latent Representations via Cross-Modal Translation and Alignment—0
Experiencers, Stimuli, or Targets: Which Semantic Roles Enable Machine Learning to Infer the Emotions?—0
I miss you babe: Analyzing Emotion Dynamics During COVID-19 Pandemic—0
Identifying Worry in Twitter: Beyond Emotion Analysis—0
Analysis of Resource-efficient Predictive Models for Natural Language Processing—0
Relation-aware Graph Attention Networks with Relational Position Encodings for Emotion Recognition in ConversationsCode1
Efficient Arabic emotion recognition using deep neural networksCode0
Pose-based Body Language Recognition for Emotion and Psychiatric Symptom Interpretation—0
Multiscale Fractal Analysis on EEG Signals for Music-Induced Emotion Recognition—0
Emotion Understanding in Videos Through Body, Context, and Visual-Semantic Embedding LossCode1
Context-Dependent Domain Adversarial Neural Network for Multimodal Emotion Recognition—0
Empirical Interpretation of Speech Emotion Perception with Attention Based Model for Speech Emotion Recognition—0
Generative Adversarial Networks in Human Emotion Synthesis:A Review—0
Seen and Unseen emotional style transfer for voice conversion with a new emotional speech datasetCode1
Speech-Based Emotion Recognition using Neural Networks and Information VisualizationCode0
CopyPaste: An Augmentation Method for Speech Emotion Recognition—0
Hybrid Backpropagation Parallel Reservoir Networks—0
Deformable Convolutional LSTM for Human Body Emotion RecognitionCode0
Neural Architecture Search of SPD Manifold NetworksCode1
Emotion recognition by fusing time synchronous and time asynchronous representations—0
Speech SIMCLR: Combining Contrastive and Reconstruction Objective for Self-supervised Speech Representation LearningCode1
Multimodal Emotion Recognition with Transformer-Based Self Supervised Feature FusionCode1
Emotion controllable speech synthesis using emotion-unlabeled dataset with the assistance of cross-domain speech emotion recognition—0
Multi-stream Attention-based BLSTM with Feature Segmentation for Speech Emotion Recognition—0
MTAG: Modal-Temporal Attention Graph for Unaligned Human Multimodal Language SequencesCode1
A Multi-Componential Approach to Emotion Recognition and the Effect of Personality—0
Dynamic Layer Customization for Noise Robust Speech Emotion Recognition in Heterogeneous Condition Training—0
Multi-Window Data Augmentation Approach for Speech Emotion Recognition—0
Facial Emotion Recognition with Noisy Multi-task AnnotationsCode1
DialogueTRM: Exploring the Intra- and Inter-Modal Emotional Behaviors in the Conversation—0
A Generalized Zero-Shot Framework for Emotion Recognition from Body Gestures—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