Papers Emotion Classification
“Emotion Classification” 태그가 달린 논문 540편 · 필터 해제
A Simple Attention-Based Mechanism for Bimodal Emotion Classification
Big data contain rich information for machine learning algorithms to utilize when learning important features during classification tasks. Human beings express their emotion using certain words, speech (tone, pitch, spee…
ClassificationDeep LearningEmotion ClassificationEmT: A Novel Transformer for Generalized Cross-subject EEG Emotion Recognition
Integrating prior knowledge of neurophysiology into neural network architecture enhances the performance of emotion decoding. While numerous techniques emphasize learning spatial and short-term temporal patterns, there h…
EEGEEG Emotion RecognitionEmotion ClassificationEmotion Recognition+2Evaluating the Effectiveness of Data Augmentation for Emotion Classification in Low-Resource Settings
Data augmentation has the potential to improve the performance of machine learning models by increasing the amount of training data available. In this study, we evaluated the effectiveness of different data augmentation …
Data AugmentationEmotion ClassificationTranslationMulti-Microphone Speech Emotion Recognition using the Hierarchical Token-semantic Audio Transformer Architecture
The performance of most emotion recognition systems degrades in real-life situations ('in the wild' scenarios) where the audio is contaminated by reverberation. Our study explores new methods to alleviate the performance…
Emotion ClassificationEmotion RecognitionSpeech Emotion RecognitionAnnotation Guidelines-Based Knowledge Augmentation: Towards Enhancing Large Language Models for Educational Text Classification
Various machine learning approaches have gained significant popularity for the automated classification of educational text to identify indicators of learning engagement -- i.e. learning engagement classification (LEC). …
Binary ClassificationClassificationEmotion ClassificationMulti-class Classification+2Enhancing Emotion Recognition in Conversation through Emotional Cross-Modal Fusion and Inter-class Contrastive Learning
The purpose of emotion recognition in conversation (ERC) is to identify the emotion category of an utterance based on contextual information. Previous ERC methods relied on simple connections for cross-modal fusion and i…
Contrastive LearningEmotion ClassificationEmotion RecognitionEmotion Recognition in ConversationST-Gait++: Leveraging spatio-temporal convolutions for gait-based emotion recognition on videos
Emotion recognition is relevant for human behaviour understanding, where facial expression and speech recognition have been widely explored by the computer vision community. Literature in the field of behavioural psychol…
Emotion ClassificationEmotion Recognitionspeech-recognitionSpeech RecognitionTransformer based neural networks for emotion recognition in conversations
This paper outlines the approach of the ISDS-NLP team in the SemEval 2024 Task 10: Emotion Discovery and Reasoning its Flip in Conversation (EDiReF). For Subtask 1 we obtained a weighted F1 score of 0.43 and placed 12 in…
Causal Language ModelingEmotion ClassificationEmotion RecognitionLanguage Modeling+3Decoding Emotions in Abstract Art: Cognitive Plausibility of CLIP in Recognizing Color-Emotion Associations
This study investigates the cognitive plausibility of a pretrained multimodal model, CLIP, in recognizing emotions evoked by abstract visual art. We employ a dataset comprising images with associated emotion labels and t…
Emotion ClassificationMachine Learning-based NLP for Emotion Classification on a Cholera X Dataset
Recent social media posts on the cholera outbreak in Hammanskraal have highlighted the diverse range of emotions people experienced in response to such an event. The extent of people's opinions varies greatly depending o…
ClassificationEmotion ClassificationRevisiting Multi-modal Emotion Learning with Broad State Space Models and Probability-guidance Fusion
Multi-modal Emotion Recognition in Conversation (MERC) has received considerable attention in various fields, e.g., human-computer interaction and recommendation systems. Most existing works perform feature disentangleme…
DisentanglementEmotion ClassificationEmotion RecognitionEmotion Recognition in Conversation+3Two in One Go: Single-stage Emotion Recognition with Decoupled Subject-context Transformer
Emotion recognition aims to discern the emotional state of subjects within an image, relying on subject-centric and contextual visual cues. Current approaches typically follow a two-stage pipeline: first localize subject…
Emotion ClassificationEmotion RecognitionEmoVIT: 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. This paradigm shows promising zero-shot results in various natural…
Emotion ClassificationEmotion RecognitionContext-Aware Siamese Networks for Efficient Emotion Recognition in Conversation
The advent of deep learning models has made a considerable contribution to the achievement of Emotion Recognition in Conversation (ERC). However, this task still remains an important challenge due to the plurality and su…
Emotion ClassificationEmotion RecognitionEmotion Recognition in ConversationMetric LearningMulti-Task Multi-Modal Self-Supervised Learning for Facial Expression Recognition
Human communication is multi-modal; e.g., face-to-face interaction involves auditory signals (speech) and visual signals (face movements and hand gestures). Hence, it is essential to exploit multiple modalities when desi…
Emotion ClassificationEmotion Recognition in ConversationFacial Expression RecognitionSelf-Supervised LearningCepstral Analysis Based Artifact Detection, Recognition and Removal for Prefrontal EEG
This paper proposes to use cepstrum for artifact detection, recognition and removal in prefrontal EEG. This work focuses on the artifact caused by eye movement. A database containing artifact-free EEG and eye movement co…
Artifact DetectionEEGEmotion ClassificationThe Power of Properties: Uncovering the Influential Factors in Emotion Classification
Facial expression-based human emotion recognition is a critical research area in psychology and medicine. State-of-the-art classification performance is only reached by end-to-end trained neural networks. Nevertheless, s…
Decision MakingEmotion ClassificationEmotion RecognitionVLLMs Provide Better Context for Emotion Understanding Through Common Sense Reasoning
Recognising emotions in context involves identifying the apparent emotions of an individual, taking into account contextual cues from the surrounding scene. Previous approaches to this task have involved the design of ex…
Common Sense ReasoningEmotion ClassificationEmotion Recognition in ContextPetKaz at SemEval-2024 Task 3: Advancing Emotion Classification with an LLM for Emotion-Cause Pair Extraction in Conversations
In this paper, we present our submission to the SemEval-2023 Task~3 "The Competition of Multimodal Emotion Cause Analysis in Conversations", focusing on extracting emotion-cause pairs from dialogs. Specifically, our appr…
Emotion-Cause Pair ExtractionEmotion ClassificationMusic Recommendation Based on Facial Emotion Recognition
Introduction: Music provides an incredible avenue for individuals to express their thoughts and emotions, while also serving as a delightful mode of entertainment for enthusiasts and music lovers. Objectives: This paper …
Emotion ClassificationEmotion RecognitionFacial Emotion RecognitionFacial Expression Recognition+2