Papers Emotion Classification
“Emotion Classification” 태그가 달린 논문 540편 · 필터 해제
Visual Affect Analysis: Predicting Emotions of Image Viewers with Vision-Language Models
Vision-language models (VLMs) show promise as tools for inferring affect from visual stimuli at scale; it is not yet clear how closely their outputs align with human affective ratings. We benchmarked nine VLMs, ranging f…
Emotion ClassificationComparative Evaluation of Machine Learning Algorithms for Affective State Recognition from Children's Drawings
Autism spectrum disorder (ASD) represents a neurodevelopmental condition characterized by difficulties in expressing emotions and communication, particularly during early childhood. Understanding the affective state of c…
Computational EfficiencyEmotion ClassificationTransfer LearningEnhancing Speech Emotion Recognition using Dynamic Spectral Features and Kalman Smoothing
Speech Emotion Recognition systems often use static features like Mel-Frequency Cepstral Coefficients (MFCCs), Zero Crossing Rate (ZCR), and Root Mean Square Energy (RMSE). Because of this, they can misclassify emotions …
Speech Emotion RecognitionEmotion ClassificationControlling Long-Horizon Behavior in Language Model Agents with Explicit State Dynamics
Large language model (LLM) agents often exhibit abrupt shifts in tone and persona during extended interaction, reflecting the absence of explicit temporal structure governing agent-level state. While prior work emphasize…
Emotion ClassificationBioNIC: Biologically Inspired Neural Network for Image Classification Using Connectomics Principles
We present BioNIC, a multi-layer feedforward neural network for emotion classification, inspired by detailed synaptic connectivity graphs from the MICrONs dataset. At a structural level, we incorporate architectural cons…
Facial Emotion RecognitionEmotion ClassificationImage ClassificationData AugmentationReal-Time Multi-Modal Embedded Vision Framework for Object Detection Facial Emotion Recognition and Biometric Identification on Low-Power Edge Platforms
Intelligent surveillance systems often handle perceptual tasks such as object detection, facial recognition, and emotion analysis independently, but they lack a unified, adaptive runtime scheduler that dynamically alloca…
Facial Emotion RecognitionEmotion ClassificationFace RecognitionObject DetectionC-GRASP: Clinically-Grounded Reasoning for Affective Signal Processing
Heart rate variability (HRV) is a pivotal noninvasive marker for autonomic monitoring; however, applying Large Language Models (LLMs) to HRV interpretation is hindered by physiological hallucinations. These include respi…
Emotion ClassificationIntegrating Attendance Tracking and Emotion Detection for Enhanced Student Engagement in Smart Classrooms
The increasing adoption of smart classroom technologies in higher education has mainly focused on automating attendance, with limited attention given to students' emotional and cognitive engagement during lectures. This …
Facial Emotion RecognitionEmotion ClassificationFace DetectionE^2-LLM: Bridging Neural Signals and Interpretable Affective Analysis
Emotion recognition from electroencephalography (EEG) signals remains challenging due to high inter-subject variability, limited labeled data, and the lack of interpretable reasoning in existing approaches. While recent …
Zero-shot GeneralizationEmotion ClassificationEmotion RecognitionAffect and Effect: Limitations of regularisation-based continual learning in EEG-based emotion classification
Generalisation to unseen subjects in EEG-based emotion classification remains a challenge due to high inter-and intra-subject variability. Continual learning (CL) poses a promising solution by learning from a sequence of…
Emotion ClassificationContinual LearningFrom Emotion Classification to Emotional Reasoning: Enhancing Emotional Intelligence in Large Language Models
This work investigates whether synthetic emotional chain-of-thought data can improve the emotional reasoning abilities of smaller open large language models (LLMs). We design a multi-agent generation pipeline that produc…
Emotion ClassificationEmotional IntelligenceEmoLoom-2B: Fast Base-Model Screening for Emotion Classification and VAD with Lexicon-Weak Supervision and KV-Off Evaluation
We introduce EmoLoom-2B, a lightweight and reproducible pipeline that turns small language models under 2B parameters into fast screening candidates for joint emotion classification and Valence-Arousal-Dominance predicti…
Emotion ClassificationEmotion classification using EEG headset signals and Random Forest
Emotions are one of the important components of the human being, thus they are a valuable part of daily activities such as interaction with people, decision making and learning. For this reason, it is important to detect…
Emotion ClassificationDecision MakingConfidence-Credibility Aware Weighted Ensembles of Small LLMs Outperform Large LLMs in Emotion Detection
This paper introduces a confidence-weighted, credibility-aware ensemble framework for text-based emotion detection, inspired by Condorcet's Jury Theorem (CJT). Unlike conventional ensembles that often rely on homogeneous…
Emotion ClassificationSentiment Analysis and Emotion Classification using Machine Learning Techniques for Nagamese Language -- A Low-resource Language
The Nagamese language, a.k.a Naga Pidgin, is an Assamese-lexified creole language developed primarily as a means of communication in trade between the people from Nagaland and people from Assam in the north-east India. S…
Emotion ClassificationSentiment AnalysisStory2MIDI: Emotionally Aligned Music Generation from Text
In this paper, we introduce Story2MIDI, a sequence-to-sequence Transformer-based model for generating emotion-aligned music from a given piece of text. To develop this model, we construct the Story2MIDI dataset by mergin…
Emotion ClassificationSentiment AnalysisMusic GenerationAnalyzing Image Beyond Visual Aspect: Image Emotion Classification via Multiple-Affective Captioning
Image emotion classification (IEC) is a longstanding research field that has received increasing attention with the rapid progress of deep learning. Although recent advances have leveraged the knowledge encoded in pre-tr…
Emotion ClassificationSemantic SimilarityBack to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations
Counterfactual explanations (CFEs) are minimal and semantically meaningful modifications of the input of a model that alter the model predictions. They highlight the decisive features the model relies on, providing contr…
Emotion ClassificationAction ClassificationCan LLMs Faithfully Explain Themselves in Low-Resource Languages? A Case Study on Emotion Detection in Persian
Large language models (LLMs) are increasingly used to generate self-explanations alongside their predictions, a practice that raises concerns about the faithfulness of these explanations, especially in low-resource langu…
Emotion ClassificationEEG Emotion Recognition Through Deep Learning
An advanced emotion classification model was developed using a CNN-Transformer architecture for emotion recognition from EEG brain wave signals, effectively distinguishing among three emotional states, positive, neutral …
EEG Emotion RecognitionEmotion Classification