Emotion Recognition
465 papers with code • 7 benchmarks • 45 datasets
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
Benchmarks
These leaderboards are used to track progress in Emotion Recognition
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Libraries
Use these libraries to find Emotion Recognition models and implementationsDatasets
Hateful Memes
FER2013
Aff-Wild
CARER
SEED
MSP-IMPROV
Aff-Wild2
ISEAR
Hateful Memes Challenge
EmotionLines
Subtasks
Latest papers
Active Learning with Task Adaptation Pre-training for Speech Emotion Recognition
To address these issues, we propose an active learning (AL)-based fine-tuning framework for SER, called \textsc{After}, that leverages task adaptation pre-training (TAPT) and AL methods to enhance performance and efficiency.
A Systematic Evaluation of Adversarial Attacks against Speech Emotion Recognition Models
In summary, this work contributes to the understanding of the robustness of CNN-LSTM models, particularly in SER scenarios, and the impact of AEs.
MultiMAE-DER: Multimodal Masked Autoencoder for Dynamic Emotion Recognition
Peihao-Xiang/MultiMAE-DFER
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In comparison to state-of-the-art multimodal supervised learning models for dynamic emotion recognition, MultiMAE-DER enhances the weighted average recall (WAR) by 4. 41% on the RAVDESS dataset and by 2. 06% on the CREMAD.
MER 2024: Semi-Supervised Learning, Noise Robustness, and Open-Vocabulary Multimodal Emotion Recognition
In addition to expanding the dataset size, we introduce a new track around open-vocabulary emotion recognition.
EmoVIT: Revolutionizing Emotion Insights with Visual Instruction Tuning
Visual Instruction Tuning represents a novel learning paradigm involving the fine-tuning of pre-trained language models using task-specific instructions.
CAGE: Circumplex Affect Guided Expression Inference
wagner-niklas/cage_expression_inference
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Using a small-scaled MaxViT-based model architecture, we evaluate the impact of discrete expression category labels in training with the continuous valence and arousal labels.
Cooperative Sentiment Agents for Multimodal Sentiment Analysis
smwanghhh/co-sa
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In this paper, we propose a new Multimodal Representation Learning (MRL) method for Multimodal Sentiment Analysis (MSA), which facilitates the adaptive interaction between modalities through Cooperative Sentiment Agents, named Co-SA.
MMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression Recognition in-the-wild
Within the field of multimodal DFER, recent methods have focused on exploiting advances of self-supervised learning (SSL) for pre-training of strong multimodal encoders.
Resolve Domain Conflicts for Generalizable Remote Physiological Measurement
Remote photoplethysmography (rPPG) technology has become increasingly popular due to its non-invasive monitoring of various physiological indicators, making it widely applicable in multimedia interaction, healthcare, and emotion analysis.
What is Learnt by the LEArnable Front-end (LEAF)? Adapting Per-Channel Energy Normalisation (PCEN) to Noisy Conditions
There is increasing interest in the use of the LEArnable Front-end (LEAF) in a variety of speech processing systems.
