Papers Emotion Classification
“Emotion Classification” 태그가 달린 논문 540편 · 필터 해제
Reinforcing Stereotypes of Anger: Emotion AI on African American Vernacular English
Automated emotion detection is widely used in applications ranging from well-being monitoring to high-stakes domains like mental health and hiring. However, models often rely on annotations that reflect dominant cultural…
Emotion ClassificationEmotion RecognitionGSE: Evaluating Sticker Visual Semantic Similarity via a General Sticker Encoder
Stickers have become a popular form of visual communication, yet understanding their semantic relationships remains challenging due to their highly diverse and symbolic content. In this work, we formally {define the Stic…
Emotion ClassificationSemantic SimilarityFederated Dialogue-Semantic Diffusion for Emotion Recognition under Incomplete Modalities
Multimodal Emotion Recognition in Conversations (MERC) enhances emotional understanding through the fusion of multimodal signals. However, unpredictable modality absence in real-world scenarios significantly degrades the…
Multimodal Emotion RecognitionEmotion ClassificationFederated LearningAfriMTEB and AfriE5: Benchmarking and Adapting Text Embedding Models for African Languages
Text embeddings are an essential building component of several NLP tasks such as retrieval-augmented generation which is crucial for preventing hallucinations in LLMs. Despite the recent release of massively multilingual…
Emotion ClassificationHate Speech DetectionIntent DetectionegoEMOTION: Egocentric Vision and Physiological Signals for Emotion and Personality Recognition in Real-World Tasks
Understanding affect is central to anticipating human behavior, yet current egocentric vision benchmarks largely ignore the person's emotional states that shape their decisions and actions. Existing tasks in egocentric p…
Emotion ClassificationMixup Helps Understanding Multimodal Video Better
Multimodal video understanding plays a crucial role in tasks such as action recognition and emotion classification by combining information from different modalities. However, multimodal models are prone to overfitting s…
Emotion ClassificationAction RecognitionParallel Tokenizers: Rethinking Vocabulary Design for Cross-Lingual Transfer
Tokenization defines the foundation of multilingual language models by determining how words are represented and shared across languages. However, existing methods often fail to support effective cross-lingual transfer b…
Representation LearningEmotion ClassificationCross-Lingual TransferHate Speech DetectionDEAP DIVE: Dataset Investigation with Vision transformers for EEG evaluation
Accurately predicting emotions from brain signals has the potential to achieve goals such as improving mental health, human-computer interaction, and affective computing. Emotion prediction through neural signals offers …
Emotion ClassificationTeaching AI to Feel: A Collaborative, Full-Body Exploration of Emotive Communication
Commonaiverse is an interactive installation exploring human emotions through full-body motion tracking and real-time AI feedback. Participants engage in three phases: Teaching, Exploration and the Cosmos Phase, collabor…
Emotion ClassificationLeveraging IndoBERT and DistilBERT for Indonesian Emotion Classification in E-Commerce Reviews
Understanding emotions in the Indonesian language is essential for improving customer experiences in e-commerce. This study focuses on enhancing the accuracy of emotion classification in Indonesian by leveraging advanced…
Emotion ClassificationData AugmentationKPoEM: A Human-Annotated Dataset for Emotion Classification and RAG-Based Poetry Generation in Korean Modern Poetry
This study introduces KPoEM (Korean Poetry Emotion Mapping), a novel dataset that serves as a foundation for both emotion-centered analysis and generative applications in modern Korean poetry. Despite advancements in NLP…
Emotion ClassificationSpatiotemporal EEG-Based Emotion Recognition Using SAM Ratings from Serious Games with Hybrid Deep Learning
Recent advancements in EEG-based emotion recognition have shown promising outcomes using both deep learning and classical machine learning approaches; however, most existing studies focus narrowly on binary valence predi…
Emotion ClassificationEmotion RecognitionEmotion Transfer with Enhanced Prototype for Unseen Emotion Recognition in Conversation
Current Emotion Recognition in Conversation (ERC) research follows a closed-domain assumption. However, there is no clear consensus on emotion classification in psychology, which presents a challenge for models when it c…
Emotion Recognition in ConversationEmotion ClassificationAmplifying Emotional Signals: Data-Efficient Deep Learning for Robust Speech Emotion Recognition
Speech Emotion Recognition (SER) presents a significant yet persistent challenge in human-computer interaction. While deep learning has advanced spoken language processing, achieving high performance on limited datasets …
Speech Emotion RecognitionEmotion ClassificationTransfer LearningData AugmentationDream to Chat: Model-based Reinforcement Learning on Dialogues with User Belief Modeling
World models have been widely utilized in robotics, gaming, and auto-driving. However, their applications on natural language tasks are relatively limited. In this paper, we construct the dialogue world model, which coul…
Reinforcement LearningEmotion ClassificationRISE: Enhancing VLM Image Annotation with Self-Supervised Reasoning
Vision-Language Models (VLMs) struggle with complex image annotation tasks, such as emotion classification and context-driven object detection, which demand sophisticated reasoning. Standard Supervised Fine-Tuning (SFT) …
Reinforcement LearningEmotion ClassificationObject DetectionDRKF: Decoupled Representations with Knowledge Fusion for Multimodal Emotion Recognition
Multimodal emotion recognition (MER) aims to identify emotional states by integrating and analyzing information from multiple modalities. However, inherent modality heterogeneity and inconsistencies in emotional cues rem…
Multimodal Emotion RecognitionRepresentation LearningEmotion ClassificationMSGM: A Multi-Scale Spatiotemporal Graph Mamba for EEG Emotion Recognition
EEG-based emotion recognition struggles with capturing multi-scale spatiotemporal dynamics and ensuring computational efficiency for real-time applications. Existing methods often oversimplify temporal granularity and sp…
Computational EfficiencyEEG Emotion RecognitionEmotion ClassificationBacktranslation and paraphrasing in the LLM era? Comparing data augmentation methods for emotion classification
Numerous domain-specific machine learning tasks struggle with data scarcity and class imbalance. This paper systematically explores data augmentation methods for NLP, particularly through large language models like GPT. …
Emotion ClassificationData AugmentationNonverbalTTS: A Public English Corpus of Text-Aligned Nonverbal Vocalizations with Emotion Annotations for Text-to-Speech
Current expressive speech synthesis models are constrained by the limited availability of open-source datasets containing diverse nonverbal vocalizations (NVs). In this work, we introduce NonverbalTTS (NVTTS), a 17-hour …
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Emotion ClassificationExpressive Speech Synthesis+5