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/yuanyuan-yuan/neural-coverage
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/yuanyuanli85/tf-hrnet
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/yudhik11/MER-lyrics-Transformer
Transformer-based approach towards music emotion recognition from lyrics
https://github.com/yuekai/PNopt
Proximal Newton-type methods for minimizing composite functions
https://github.com/yuelinan/c3vg
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/yueyongjiao/Transformer-Contextual
https://github.com/yueyongjiao/attention_is_all_you_need_pytorch
https://github.com/yuhangT/tf_bert
https://github.com/yukezhu/tensorflow-reinforce
https://github.com/yukichou/PET
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/yumaloop/CV_report2019
https://github.com/yumoh/speech-keras
https://github.com/yusme/DDPG
https://github.com/yuzhimanhua/Multi-BioNER
Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning
https://github.com/yxgeee/MMT
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/yxgeee/SpCL
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/yydai/bert_test
https://github.com/zaaztt/bert_chinese_example
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/zackzhao1/BackgroundMattingV2-paddle
https://github.com/zafercavdar/fasttext-langdetect
https://github.com/zankner/Transformer
https://github.com/zapplea/bert
https://github.com/zaradana/Fast_BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/zbwxp/NRD_decoder
Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation
https://github.com/zbyte64/pytorch-dagsearch
Progressive Neural Networks for Transfer Learning in Emotion Recognition
https://github.com/zdk123/SpiecEasi
Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models
https://github.com/zehuiwu/SpeechCueLLM
Beyond Silent Letters: Amplifying LLMs in Emotion Recognition with Vocal Nuances
https://github.com/zer0sh0t/artificial_intelligence/tree/master/language_models/bidirectional_encoder_representations_from_transformers
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/zer0sh0t/artificial_intelligence/tree/master/transformer
https://github.com/zeroqiaoba/affectgpt
MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning
https://github.com/zeroqiaoba/explainable-multimodal-emotion-reasoning
MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning
https://github.com/zgctroy/layoutdiffusion
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/zh320/realtime-semantic-segmentation-pytorch
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
https://github.com/zhang-huihui/git-repository
https://github.com/zhangbo2008/best_pytorch_transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.