Emotion Recognition
9개 벤치마크 · 논문 2,254편 · 이 태스크의 논문 보기 →
Benchmarks
RAVDESS
Emomusic
SEED
FER2013
EMOTIC
MPED
MSP-Podcast
MaSaC_ERC
Most implemented
EEG-Based Emotion Recognition Using Regularized Graph Neural Networks
Multimodal Speech Emotion Recognition and Ambiguity Resolution
Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors
DialogXL: All-in-One XLNet for Multi-Party Conversation Emotion Recognition
Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts
Papers
AURAL: Adaptive Latent Reasoning with Joint Chunk for Speech Language Models
Model intelligence and fast response jointly shape the quality of interaction with speech language models, yet remain difficult to achieve together. Explicit chain-of-thought (CoT) improves reasoning and audio understand…
Emotion RecognitionSEA-SpeechBench: A Large-Scale Multitask Benchmark for Speech Understanding Across Southeast Asia
The rapid advancement of audio and multimodal large language models has unlocked transformative speech understanding capabilities, yet evaluation frameworks remain predominantly English-centric, leaving Southeast Asian (…
Emotion RecognitionSpeaker RecognitionSpeech RecognitionQuestion AnsweringCross-lingual Functional Vectors for Emotion Detection in Large Language Models
Function vectors (FVs) have recently emerged as a promising mechanism for steering the behavior of large language models (LLMs) by injecting task-specific latent direction representations derived from in-context demonstr…
Emotion RecognitionDissonance Spectrum explicitly models perceptual frequency interactions for better music understanding
Conventional music representations describe acoustic energy over time and frequency but do not explicitly expose relations among simultaneous frequency components. We introduce the \emph{Dissonance Spectrum} (DS), a nonn…
Music Question AnsweringEmotion RecognitionMulti-Feature Riemannian Hypergraph for Online Test-Time Adaptation of Motor Imagery Brain-Computer Interface
In clinical motor imagery brain-computer interface (MI-BCI) decoding, cross-day transferability and online operation remain two critical challenges. Hypergraphs can improve transferability by capturing higher-order sampl…
Test-time AdaptationEmotion RecognitionTransfer LearningProFocus: Interpreting Affective Experience in Artistic Images with Progressive Visual Focusing
Interpreting the emotional responses triggered by images is central to achieving emotional intelligence. Compared with natural images, visual art is intentionally created to elicit emotional responses from its viewers th…
Representation LearningEmotional IntelligenceEmotion Recognition