https://github.com/hszhao/ICNet
공식 코드ICNet for Real-Time Semantic Segmentation on High-Resolution Images
Papers with Code 아카이브
Speech Emotion Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 4
- 평가표
- 17
- 보존된 평가 기록
- 48
Speech Emotion Recognition is a task of speech processing and computational paralinguistics that aims to recognize and categorize the emotions expressed in spoken language. The goal is to determine the emotional state of a speaker, such as happiness, anger, sadness, or frustration, from their speech patterns, such as prosody, pitch, and rhythm.
For multimodal emotion recognition, please upload your result to Multimodal Emotion Recognition on IEMOCAP
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/htseq/htseq
공식 코드Analysing high-throughput sequencing data in Python with HTSeq 2.0
https://github.com/huawei-noah/hebo
공식 코드High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning
https://github.com/huawei-noah/noah-research/tree/master/BO/T-LBO
공식 코드High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning
https://github.com/humbug-mosquito/humbugdb
공식 코드https://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/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/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/isaaconline/speat
공식 코드https://github.com/isabelladegen/insulin-need
공식 코드https://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/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
https://github.com/jarrycyx/unn
공식 코드CUTS+: High-dimensional Causal Discovery from Irregular Time-series
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.