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/cstorm125/thai2fit
Breaking the Softmax Bottleneck: A High-Rank RNN Language Model
https://github.com/cuber2460/bert
https://github.com/cui0523/Code6/tree/main/dialogxl
DialogXL: All-in-One XLNet for Multi-Party Conversation Emotion Recognition
https://github.com/cuiding/RTHN-master
RTHN: A RNN-Transformer Hierarchical Network for Emotion Cause Extraction
https://github.com/cuiding/RTHN-master2
RTHN: A RNN-Transformer Hierarchical Network for Emotion Cause Extraction
https://github.com/currentsapi/extractnet
https://github.com/currentsapi/fastlangid
https://github.com/currentslab/extractnet
https://github.com/cvpaperchallenge/FashionCultureDataBase_DLoader
https://github.com/cyb1212/Monocular-Depth-Estimation-vis-Transfer-learning-Winter-school-
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/cybertronai/megatron-lm
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/cyberzhg/keras-pos-embd
https://github.com/czero69/acomoeye-NN
https://github.com/d-shivam/Pose-estimation-based-action-recognition-for-help-Situation-Identification
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/daheyinyin/ICNet
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
https://github.com/damien911224/theWorldInSafety
Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/dan-lennox/ml-udacity-quadcopter-rl
https://github.com/daniallin/DenseDepth-Pytorch
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/danini/homography-from-sift-features
Homography from two orientation- and scale-covariant features
https://github.com/danlim-wz/analysis_of_social_media_data_about_singapore
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/danrugeles/Heron
SAME but Different: Fast and High-Quality Gibbs Parameter Estimation
https://github.com/darkrush/meta_learn
https://github.com/darr/nerbert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/darr/nlp_work
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/datacrisis/BOBBY2
https://github.com/david-yoon/multimodal-speech-emotion
https://github.com/davidalbertonogueira/NLP-tutorials
https://github.com/davidbellamy/labrador
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/davidsvaughn/dts-tf
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/davidzhou9/DeepVol
Solving high-dimensional partial differential equations using deep learning
https://github.com/davisking/dlib-models
https://github.com/dbmdz/historic-ner
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/dccuchile/CC6204
https://github.com/dchetelat/acer
https://github.com/ddajing/multilayer-cnn-text-classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.