https://github.com/ClaudiuCreanga/semeval-2024-task-8
공식 코드Transformer based neural networks for emotion recognition in conversations
Papers with Code 아카이브
Emotion Classification
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Emotion classification, or emotion categorization, is the task of recognising emotions to classify them into the corresponding category. Given an input, classify it as 'neutral or no emotion' or as one, or more, of several given emotions that best represent the mental state of the subject's facial expression, words, and so on. Some example benchmarks include ROCStories, Many Faces of Anger (MFA), and GoEmotions. Models can be evaluated using metrics such as the Concordance Correlation Coefficient (CCC) and the Mean Squared Error (MSE).
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ControlNet/MARLIN
공식 코드MARLIN: Masked Autoencoder for facial video Representation LearnINg
https://github.com/ETZET/SpeechEmotionAVLearning
공식 코드Learning Arousal-Valence Representation from Categorical Emotion Labels of Speech
https://github.com/HSE-asavchenko/face-emotion-recognition
공식 코드https://github.com/IvonaTau/emotionaldan
공식 코드https://github.com/JumanaFM/emoviz
공식 코드Speech-Based Emotion Recognition using Neural Networks and Information Visualization
https://github.com/Natooz/music-modeling-time-duration
공식 코드Impact of time and note duration tokenizations on deep learning symbolic music modeling
https://github.com/SenticNet/conv-emotion
공식 코드DialogueRNN: An Attentive RNN for Emotion Detection in Conversations
https://github.com/StonyBrookNLP/emotion-label-semantics
공식 코드https://github.com/WadeYin9712/SentiBERT
공식 코드SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semantics
https://github.com/YanhaoHuang23/FAT
공식 코드A novel Fourier Adjacency Transformer for advanced EEG emotion recognition
https://github.com/ZizhouJia/PDANet
공식 코드PDANet: Polarity-consistent Deep Attention Network for Fine-grained Visual Emotion Regression
https://github.com/adlnlp/stockemotions
공식 코드https://github.com/aimmemotion/emovit
공식 코드EmoVIT: Revolutionizing Emotion Insights with Visual Instruction Tuning
https://github.com/alexandra-chron/wassa-2018
공식 코드NTUA-SLP at IEST 2018: Ensemble of Neural Transfer Methods for Implicit Emotion Classification
https://github.com/alieto/DEGARI
공식 코드https://github.com/allambert/torch_itl
공식 코드https://github.com/arman-rayan-sharif/arman-text-emotion
공식 코드ArmanEmo: A Persian Dataset for Text-based Emotion Detection
https://github.com/arminarj/empathic-grounding
공식 코드https://github.com/atharva-naik/VADEC
공식 코드Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task Approach
https://github.com/bloodraven66/ssl_aai
공식 코드https://github.com/bothe/EDAs
공식 코드EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators
https://github.com/bothe/dialogue-act-recognition
공식 코드EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators
https://github.com/chengq220/Facial_Perturb
공식 코드https://github.com/david-yoon/attentive-modality-hopping-for-SER
공식 코드Attentive Modality Hopping Mechanism for Speech Emotion Recognition
https://github.com/dinobby/hypemo
공식 코드Label-Aware Hyperbolic Embeddings for Fine-grained Emotion Classification
https://github.com/dteodore/emotionarcs
공식 코드Evaluating Emotion Arcs Across Languages: Bridging the Global Divide in Sentiment Analysis
https://github.com/dutir-ysq/emometa
공식 코드EmoMeta: A Multimodal Dataset for Fine-grained Emotion Classification in Chinese Metaphors
https://github.com/dykuang/bci-attention
공식 코드A Monotonicity Constrained Attention Module for Emotion Classification with Limited EEG Data
https://github.com/emorynlp/CMCL-2021
공식 코드Enhancing Cognitive Models of Emotions with Representation Learning
https://github.com/fmplaza/zsl_nli_emotion_prompts
공식 코드Natural Language Inference Prompts for Zero-shot Emotion Classification in Text across Corpora
https://github.com/ggcr/tinyemo
공식 코드TinyEmo: Scaling down Emotional Reasoning via Metric Projection
https://github.com/gguibon/protoseq
공식 코드Few-Shot Emotion Recognition in Conversation with Sequential Prototypical Networks
https://github.com/google-research/google-research
공식 코드https://github.com/griko/vanpy
공식 코드https://github.com/guangyizhangbci/parse
공식 코드PARSE: Pairwise Alignment of Representations in Semi-Supervised EEG Learning for Emotion Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.