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/sungjoonpark/emotiondetection
공식 코드https://github.com/tnhaider/poetry-emotion
공식 코드https://github.com/ttslr/strengthnet
공식 코드Accurate Emotion Strength Assessment for Seen and Unseen Speech Based on Data-Driven Deep Learning
https://github.com/tub-cv-group/conclugen
공식 코드Multi-Task Multi-Modal Self-Supervised Learning for Facial Expression Recognition
https://github.com/unslothai/unsloth
공식 코드https://github.com/vadel/acpd
공식 코드Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions
https://github.com/varsha33/lcl_loss
공식 코드Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification
https://github.com/vijay4313/proxemo
공식 코드https://github.com/wazenmai/MIDI-BERT
공식 코드BERT-like Pre-training for Symbolic Piano Music Classification Tasks
https://github.com/werywjw/sep-cvdl
공식 코드GiMeFive: Towards Interpretable Facial Emotion Classification
https://github.com/xxxxxxxxy/memotion3-SEFusion
공식 코드https://github.com/yangzhou12/ncrl
공식 코드None Class Ranking Loss for Document-Level Relation Extraction
https://github.com/ydkwim/ctal
공식 코드CTAL: Pre-training Cross-modal Transformer for Audio-and-Language Representations
https://github.com/yelboudouri/EmoNeXt
공식 코드https://github.com/yi-ding-cs/emt
공식 코드EmT: A Novel Transformer for Generalized Cross-subject EEG Emotion Recognition
https://github.com/yyjmjc/temporal-emotion-localization-in-videos
공식 코드https://github.com/zeroQiaoba/EmotiW2018
공식 코드Investigation of Multimodal Features, Classifiers and Fusion Methods for Emotion Recognition
https://github.com/zhanglinfeng1997/Sentiment-Analysis-via-GCN
공식 코드Fine-Grained Emotion Classification of Chinese Microblogs Based on Graph Convolution Networks
https://github.com/zhongpeixiang/SemEval2019-Task3-EmotionDetection
공식 코드ntuer at SemEval-2019 Task 3: Emotion Classification with Word and Sentence Representations in RCNN
https://github.com/zyzhangustc/mellm
공식 코드MELLM: Exploring LLM-Powered Micro-Expression Understanding Enhanced by Subtle Motion Perception
https://github.com/AmrElsersy/Emotions-Recognition
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://github.com/Andyeyeye/MTANet
Facial Affect Recognition in the Wild Using Multi-Task Learning Convolutional Network
https://github.com/CaedenZ/distractionModel
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://github.com/CodeChefVIT/VOID
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://github.com/FengJiaChunFromSYSU/ntua-slp-semeval2018
https://github.com/KomorebiLHX/Emotion-Recognition-in-Conversations
DialogueRNN: An Attentive RNN for Emotion Detection in Conversations
https://github.com/MagnusXu/Speech-Emotion-Recognition-Capstone-Project
https://github.com/NVIDIA/sentiment-discovery
Practical Text Classification With Large Pre-Trained Language Models
https://github.com/Natooz/MidiTok
MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Training
https://github.com/OwinoMichael/FinBERT-Sentiment-Analysis-and-MTS-Temp-Att.-LSTM-
https://github.com/SER-2020-Project-ZX/Reference
https://github.com/ajinkyabedekar/Face-to-Emoji
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://github.com/alexandra-chron/ntua-slp-semeval2018
https://github.com/ankurbhatia24/MULTIMODAL-EMOTION-RECOGNITION
https://github.com/anshu123priya/Real-v-s-Fake-Emotion-Challenge
Real-time Convolutional Neural Networks for Emotion and Gender Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.