Emotion Recognition
464 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
Affective-NLI: Towards Accurate and Interpretable Personality Recognition in Conversation
To utilize affectivity within dialog content for accurate personality recognition, we fine-tuned a pre-trained language model specifically for emotion recognition in conversations, facilitating real-time affective annotations for utterances.
MIPS at SemEval-2024 Task 3: Multimodal Emotion-Cause Pair Extraction in Conversations with Multimodal Language Models
This paper presents our winning submission to Subtask 2 of SemEval 2024 Task 3 on multimodal emotion cause analysis in conversations.
Heterogeneity over Homogeneity: Investigating Multilingual Speech Pre-Trained Models for Detecting Audio Deepfake
To validate our hypothesis, we extract representations from state-of-the-art (SOTA) PTMs including monolingual, multilingual as well as PTMs trained for speaker and emotion recognition, and evaluated them on ASVSpoof 2019 (ASV), In-the-Wild (ITW), and DECRO benchmark databases.
Emotion-Anchored Contrastive Learning Framework for Emotion Recognition in Conversation
yu-fangxu/eacl
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To achieve this, we utilize label encodings as anchors to guide the learning of utterance representations and design an auxiliary loss to ensure the effective separation of anchors for similar emotions.
MMCert: Provable Defense against Adversarial Attacks to Multi-modal Models
WYT8506/MultimodalCertification
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Moreover, we compare our MMCert with a state-of-the-art certified defense extended from unimodal models.
Colour and Brush Stroke Pattern Recognition in Abstract Art using Modified Deep Convolutional Generative Adversarial Networks
Deceptrax123/Pattern-Recognition-in-Abstract-art-
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Further this paper explores the generated latent space by performing random walks to understand vector relationships between brush strokes and colours in the abstract art space and a statistical analysis of unstable outputs after a certain period of GAN training and compare its significant difference.
Unlocking the Emotional States of High-Risk Suicide Callers through Speech Analysis
alaaNfissi/Unlocking-the-Emotional-States-of-High-Risk-Suicide-Callers-through-Speech-Analysis
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IEEE 18th International Conference on Semantic Computing (ICSC) 2024
In light of these challenges, we present a novel end-to-end (E2E) method for speech emotion recognition (SER) as a mean of detecting changes in emotional state, that may indicate a high risk of suicide.
Recursive Joint Cross-Modal Attention for Multimodal Fusion in Dimensional Emotion Recognition
praveena2j/rjcma
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In particular, we compute the attention weights based on cross-correlation between the joint audio-visual-text feature representations and the feature representations of individual modalities to simultaneously capture intra- and intermodal relationships across the modalities.
Iterative Feature Boosting for Explainable Speech Emotion Recognition
In speech emotion recognition (SER), using pre- defined features without considering their practical importance may lead to high dimensional datasets, including redundant and irrelevant information.
Joint Multimodal Transformer for Emotion Recognition in the Wild
PoloWlg/Joint-Multimodal-Transformer-6th-ABAW
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Multimodal emotion recognition (MMER) systems typically outperform unimodal systems by leveraging the inter- and intra-modal relationships between, e. g., visual, textual, physiological, and auditory modalities.
