Papers with Code 아카이브
Emotion Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 10
- 보존된 평가 기록
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Emotion Recognition is an important area of research to enable effective human-computer interaction. Human emotions can be detected using speech signal, facial expressions, body language, and electroencephalography (EEG). Source: Using Deep Autoencoders for Facial Expression Recognition
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ZhiqiWang12-hash/text_audio_classification
Learning Alignment for Multimodal Emotion Recognition from Speech
https://github.com/ZiningZhu/feng-hirst-rst-parser
Two-pass Discourse Segmentation with Pairing and Global Features
https://github.com/ZiyangY/IndProject-RL-in-Supply-chain
https://github.com/ZmeiGorynych/transformer_pytorch
https://github.com/Zuhashaik/Multi-Party-DialoZ
https://github.com/ZurichNLP/sockeye
https://github.com/a-vzer/waveletflowpytorch
Wavelet Flow: Fast Training of High Resolution Normalizing Flows
https://github.com/a414351664/Bert_MRC
https://github.com/a414351664/my_git_laser
https://github.com/aakashrkaku/seq2seq_hrar
Sequence-to-Sequence Modeling for Action Identification at High Temporal Resolution
https://github.com/aaron-wheeler/SFIArtificialStockMarket.jl
The effects of incentives, social norms, and employees' values on work performance
https://github.com/aaronH45/sentimentAnalysis
https://github.com/aaronHuang0121/AI-CUP-2020
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/aashutj/Multimodal-Multiparty-Emotion-Recognition-System-in-Conversation-Text-Mode-Only-
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
https://github.com/abbadka/quadcopter
https://github.com/abdulfatir/gan-metrics-pytorch
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/abhaskumarsinha/Keras-implementation-of-Transformer-Architecture
https://github.com/abhaskumarsinha/Keras-implementation-of-Transformer-Architecture/blob/main/Transformer.py
https://github.com/abhaskumarsinha/MinimalGPT
https://github.com/abhi1kumar/hrnet_pose_single_gpu
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/abhi8585/GeneratedART-NFT-VISION
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/abhiagg92/bert_sentiment
https://github.com/abhilash1910/BERTSimilarity
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/abhilashreddys/Fake-News-Article
https://github.com/abhineet/sentence_classification_pubmed_scibert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/abmitra84/Language_Modeling
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/abmitra84/Machine_Translation
https://github.com/adamx97/Data-Science-Advanced-Capstone
https://github.com/adi2103/AML-CoVe
https://github.com/adik993/ppo-pytorch
High-Dimensional Continuous Control Using Generalized Advantage Estimation
https://github.com/adsieg/Speech
Multimodal Speech Emotion Recognition and Ambiguity Resolution
https://github.com/advimman/lama
Feature Refinement to Improve High Resolution Image Inpainting
https://github.com/af-ai-center/bert
https://github.com/af-ai-center/nerblackbox
https://github.com/afrozloya/charrec
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.