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/matsuolab/cmu-multimodalsdk
Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors
https://github.com/mattangus/fast-semantic-segmentation
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
https://github.com/matthewsparr/Reinforcement-Learning-Lesson
https://github.com/mattiasxu/VQVAE-2
https://github.com/mattiasxu/Video-VQVAE
https://github.com/maxjcohen/transformer
https://github.com/maxmatical/pytorch-projects
https://github.com/maxudong1993/mybert
https://github.com/mayoor/attention_network_experiments
https://github.com/mayuelala/followyourpose
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/mayurnewase/Quora-Bert
https://github.com/mayurnewase/Translation
https://github.com/mcgridles/LENS
Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/mdt48/semantic-segmentation-pytorch
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/meelfy/pytorch_pretrained_bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/megvii-research/basecls/tree/main/zoo/public/hrnet
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/mehdibenamorr/Token-Positional-Bias
https://github.com/mehdibenamorr/bert-positional-bias-ner
https://github.com/meizi1114/bert
https://github.com/melikaemami/Generalization-Error-of-GLMs
Generalization Error of Generalized Linear Models in High Dimensions
https://github.com/metavoiceio/metavoice-src
https://github.com/mevers/animated_dimensionality_reduction
https://github.com/mhamedLmarbouh/Attention-is-all-you-need
https://github.com/mhaseeb123/DeepMSim
https://github.com/michaelpatrickpurcell/graph-dp
Differentially-Private Two-Party Egocentric Betweenness Centrality
https://github.com/michaeltrs/DeepSatData
Context-self contrastive pretraining for crop type semantic segmentation
https://github.com/microcoder-py/attn-is-all-you-need
https://github.com/microndla/mobilevitv3
https://github.com/microsoft/art
MosAIc: Finding Artistic Connections across Culture with Conditional Image Retrieval
https://github.com/microsoft/onnxruntime-training-examples
https://github.com/microsoft/ptgnn
https://github.com/microsoft/visual-chatgpt
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/midas-research/calling-out-bluff
https://github.com/midas-research/gupshup
BHAAV- A Text Corpus for Emotion Analysis from Hindi Stories
https://github.com/mightypirate1/DRL-Tetris
High-Dimensional Continuous Control Using Generalized Advantage Estimation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.