Papers with Code 아카이브
Emotion Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 12
- 평가표
- 10
- 보존된 평가 기록
- 18
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/Soren-Kierkegaard/BERT-for-tweet-Classifcation
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Souphis/mobile_robot_rl
https://github.com/SoyBison/gammatone
https://github.com/SpikeKing/My-Bert
https://github.com/StanfordVL/MinkowskiEngine
High-dimensional Convolutional Networks for Geometric Pattern Recognition
https://github.com/Statistics-In-Portfolio-Theory/DOSportfolio
Dynamic Shrinkage Estimation of the High-Dimensional Minimum-Variance Portfolio
https://github.com/StephanLorenzen/AuthorshipVerification
https://github.com/StevenLai1994/AM_and_LM
https://github.com/StillKeepTry/Transformer-PyTorch
https://github.com/StoneGH/bert
https://github.com/StuartCHAN/KARL
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/SubhamIO/Emotion-Recognition-in-Conversation
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
https://github.com/SumitM0432/Quora-Insincere-Questions-Classification
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Superlee506/Correlation_Filtering_Tracking_Lib
https://github.com/Susheel-1999/Sentence_Similarity
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Sushmitha-Katti/DepthModel
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/SvenKlaassen/AdaptiveDiscreteSmoothing
Adaptive Discrete Smoothing for High-Dimensional and Nonlinear Panel Data
https://github.com/SvenKlaassen/GGMtest
Uniform Inference in High-Dimensional Gaussian Graphical Models
https://github.com/SysCV/vmt
Video Mask Transfiner for High-Quality Video Instance Segmentation
https://github.com/T3chy/DDPG
https://github.com/TEAMLAB-Lecture/deep_nlp_101
https://github.com/THUNLP-MT/THUMT
https://github.com/TOSHISTATS/Classification-of-Japanese-news-with-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/TSLNIHAOGIT/bert
https://github.com/TSLNIHAOGIT/bert_run
https://github.com/TYTYTYTYTYTYTYTYTY/558-project
https://github.com/Tandon-A/emotic
https://github.com/TangShengqin/DynamicRCNN-mmdet
Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training
https://github.com/TannerFry/CS522-Final-Project
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/TeCSAR-UNCC/EfficientHRNet
EfficientHRNet: Efficient Scaling for Lightweight High-Resolution Multi-Person Pose Estimation
https://github.com/TeamLab/bert-gcn-for-paper-citation
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Tenavi/HJB_NN
Adaptive Deep Learning for High-Dimensional Hamilton-Jacobi-Bellman Equations
https://github.com/Tencent/TurboTransformers
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Text-to-Audio/AudioLCM
ProDiff: Progressive Fast Diffusion Model For High-Quality Text-to-Speech
https://github.com/The-AI-Summer/self_attention
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.