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 1451–1475 of 2041 papers

TitleStatusHype
An Iterative Emotion Interaction Network for Emotion Recognition in Conversations—0
Contextual Augmentation of Pretrained Language Models for Emotion Recognition in Conversations—0
MemoSYS at SemEval-2020 Task 8: Multimodal Emotion Analysis in Memes—0
CN-HIT-MI.T at SemEval-2020 Task 8: Memotion Analysis Based on BERT—0
SemEval-2020 Task 8: Memotion Analysis- the Visuo-Lingual Metaphor!—0
MEISD: A Multimodal Multi-Label Emotion, Intensity and Sentiment Dialogue Dataset for Emotion Recognition and Sentiment Analysis in Conversations—0
Regrexit or not Regrexit: Aspect-based Sentiment Analysis in Polarized Contexts—0
Technical Domain Identification using word2vec and BiLSTM—0
Summarize before Aggregate: A Global-to-local Heterogeneous Graph Inference Network for Conversational Emotion Recognition—0
Knowledge Aware Emotion Recognition in Textual Conversations via Multi-Task Incremental Transformer—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
Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks—0
On the use of Self-supervised Pre-trained Acoustic and Linguistic Features for Continuous Speech Emotion Recognition—0
Interpretable Image Emotion Recognition: A Domain Adaptation Approach Using Facial Expressions—0
Improving Multimodal Accuracy Through Modality Pre-training and Attention—0
Recognizing More Emotions with Less Data Using Self-supervised Transfer Learning—0
WaDeNet: Wavelet Decomposition based CNN for Speech Processing—0
NUAA-QMUL at SemEval-2020 Task 8: Utilizing BERT and DenseNet for Internet Meme Emotion AnalysisCode0
Experiencers, Stimuli, or Targets: Which Semantic Roles Enable Machine Learning to Infer the Emotions?—0
Robust Latent Representations via Cross-Modal Translation and Alignment—0
Identifying Worry in Twitter: Beyond Emotion Analysis—0
I miss you babe: Analyzing Emotion Dynamics During COVID-19 Pandemic—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