Papers with Code 아카이브
Emotion Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 12
- 평가표
- 10
- 보존된 평가 기록
- 18
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
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Camera-based implicit mind reading by capturing higher-order semantic dynamics of human gaze within environmental context
A Robust Incomplete Multimodal Low-Rank Adaptation Approach for Emotion Recognition
Dynamic Parameter Memory: Temporary LoRA-Enhanced LLM for Long-Sequence Emotion Recognition in Conversation
CAST-Phys: Contactless Affective States Through Physiological signals Database
Exploring Remote Physiological Signal Measurement under Dynamic Lighting Conditions at Night: Dataset, Experiment, and Analysis
How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models?
Emotion Detection on User Front-Facing App Interfaces for Enhanced Schedule Optimization: A Machine Learning Approach
MATER: Multi-level Acoustic and Textual Emotion Representation for Interpretable Speech Emotion Recognition
Infant Cry Emotion Recognition Using Improved ECAPA-TDNN with Multiscale Feature Fusion and Attention Enhancement
Reading Smiles: Proxy Bias in Foundation Models for Facial Emotion Recognition
Deciphering Emotions in Children Storybooks: A Comparative Analysis of Multimodal LLMs in Educational Applications
Optimizing Multilingual Text-To-Speech with Accents & Emotions
GSDNet: Revisiting Incomplete Multimodal-Diffusion from Graph Spectrum Perspective for Conversation Emotion Recognition
Towards Robust Multimodal Emotion Recognition under Missing Modalities and Distribution Shifts
Developing a High-performance Framework for Speech Emotion Recognition in Naturalistic Conditions Challenge for Emotional Attribute Prediction
MEDUSA: A Multimodal Deep Fusion Multi-Stage Training Framework for Speech Emotion Recognition in Naturalistic Conditions
Analyzing Emotions in Bangla Social Media Comments Using Machine Learning and LIME
CoMuMDR: Code-mixed Multi-modal Multi-domain corpus for Discourse paRsing in conversations
Multi-Teacher Language-Aware Knowledge Distillation for Multilingual Speech Emotion Recognition
Beyond Classification: Towards Speech Emotion Reasoning with Multitask AudioLLMs
CO-VADA: A Confidence-Oriented Voice Augmentation Debiasing Approach for Fair Speech Emotion Recognition
EMO-Debias: Benchmarking Gender Debiasing Techniques in Multi-Label Speech Emotion Recognition
Prosodic Structure Beyond Lexical Content: A Study of Self-Supervised Learning
HYFuse: Aligning Heterogeneous Speech Pre-Trained Representations in Hyperbolic Space for Speech Emotion Recognition
Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025
Towards Machine Unlearning for Paralinguistic Speech Processing
Investigating the Impact of Word Informativeness on Speech Emotion Recognition
Are Mamba-based Audio Foundation Models the Best Fit for Non-Verbal Emotion Recognition?
EfficientFER: EfficientNetv2 Based Deep Learning Approach for Facial Expression Recognition
PARROT: Synergizing Mamba and Attention-based SSL Pre-Trained Models via Parallel Branch Hadamard Optimal Transport for Speech Emotion Recognition
Source Tracing of Synthetic Speech Systems Through Paralinguistic Pre-Trained Representations
Learning More with Less: Self-Supervised Approaches for Low-Resource Speech Emotion Recognition
KEVER^2: Knowledge-Enhanced Visual Emotion Reasoning and Retrieval
MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge
What About Emotions? Guiding Fine-Grained Emotion Extraction from Mobile App Reviews
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.