Papers with Code 아카이브
Emotion Recognition
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- 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/humeai/competitions
공식 코드https://github.com/huqinghao/palquant
공식 코드PalQuant: Accelerating High-precision Networks on Low-precision Accelerators
https://github.com/hustvl/boxteacher
공식 코드BoxTeacher: Exploring High-Quality Pseudo Labels for Weakly Supervised Instance Segmentation
https://github.com/huuxiaobin/hitnet
공식 코드High-resolution Iterative Feedback Network for Camouflaged Object Detection
https://github.com/hyeonsu-jeong/toptwo
공식 코드Recovering Top-Two Answers and Confusion Probability in Multi-Choice Crowdsourcing
https://github.com/hyer/hdformer
공식 코드HDFormer: High-order Directed Transformer for 3D Human Pose Estimation
https://github.com/hyperion-ml/hyperion
공식 코드Non-Contrastive Self-Supervised Learning of Utterance-Level Speech Representations
https://github.com/hyungkwonko/umato
공식 코드https://github.com/hzli-ucas/stargraph
공식 코드StarGraph: Knowledge Representation Learning based on Incomplete Two-hop Subgraph
https://github.com/iacercalixto/visualsem
공식 코드VisualSem: A High-quality Knowledge Graph for Vision and Language
https://github.com/iag0g0mes/t2fis_driving_style
공식 코드https://github.com/ialhashim/DenseDepth
공식 코드High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/iamgroot42/nelec
공식 코드https://github.com/icbcbicc/FocusLiteNN
공식 코드FocusLiteNN: High Efficiency Focus Quality Assessment for Digital Pathology
https://github.com/iclr2023achangeofheart/meld-modality-conversion
공식 코드A Change of Heart: Improving Speech Emotion Recognition through Speech-to-Text Modality Conversion
https://github.com/icvteam/pgnet
공식 코드Pyramid Grafting Network for One-Stage High Resolution Saliency Detection
https://github.com/idansc/HighOrderAtten
공식 코드https://github.com/iflytek/minirbt
공식 코드MiniRBT: A Two-stage Distilled Small Chinese Pre-trained Model
https://github.com/iiscleap/abhinaya
공식 코드ABHINAYA -- A System for Speech Emotion Recognition In Naturalistic Conditions Challenge
https://github.com/iiscleap/care
공식 코드Leveraging Content and Acoustic Representations for Speech Emotion Recognition
https://github.com/iisys-hof/HUI-Audio-Corpus-German
공식 코드https://github.com/ikergarcia1996/t-projection
공식 코드T-Projection: High Quality Annotation Projection for Sequence Labeling Tasks
https://github.com/ilyakava/pyfst
공식 코드https://github.com/implicitdeclaration/hvaq-dataset
공식 코드https://github.com/ionur/cmb
공식 코드High-Resolution CMB Lensing Reconstruction with Deep Learning
https://github.com/irisdominguez/dataset_bias_metrics
공식 코드Metrics for Dataset Demographic Bias: A Case Study on Facial Expression Recognition
https://github.com/isaaconline/speat
공식 코드https://github.com/isabelladegen/insulin-need
공식 코드https://github.com/ispamm/mhyeeg
공식 코드Hypercomplex Multimodal Emotion Recognition from EEG and Peripheral Physiological Signals
https://github.com/ispamm/rhemo
공식 코드Learning Speech Emotion Representations in the Quaternion Domain
https://github.com/ja-che/hidimstat
공식 코드Spatially relaxed inference on high-dimensional linear models
https://github.com/jabumldev/speaker-vgg-cct
공식 코드https://github.com/jackyfl/emma_cotex_abaw4
공식 코드Affective Behaviour Analysis Using Pretrained Model with Facial Priori
https://github.com/jakobrunge/tigramite
공식 코드High-recall causal discovery for autocorrelated time series with latent confounders
https://github.com/janenie/espn-nba-data
공식 코드Operations Guided Neural Networks for High Fidelity Data-To-Text Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.