https://github.com/CaiShilv/HiFi-VRIC
공식 코드High-Fidelity Variable-Rate Image Compression via Invertible Activation Transformation
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/CenekAlbl/TwoViewUnsynch
공식 코드https://github.com/ChenhongyiYang/QueryDet-PyTorch
공식 코드QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small Object Detection
https://github.com/ChopinSharp/ref-nms
공식 코드Ref-NMS: Breaking Proposal Bottlenecks in Two-Stage Referring Expression Grounding
https://github.com/Circuitscape/Circuitscape.jl
공식 코드Circuitscape in Julia: High Performance Connectivity Modelling to Support Conservation Decisions
https://github.com/CogComp/zoe
공식 코드https://github.com/Cold-Winter/BachGAN
공식 코드BachGAN: High-Resolution Image Synthesis from Salient Object Layout
https://github.com/CompVis/taming-transformers
공식 코드https://github.com/Cynetics/MSGNet
공식 코드Generating Annotated High-Fidelity Images Containing Multiple Coherent Objects
https://github.com/DAI-Lab/SteganoGAN
공식 코드https://github.com/DPautoGAN/DPautoGAN
공식 코드Differentially Private Synthetic Mixed-Type Data Generation For Unsupervised Learning
https://github.com/Daikenan/LTMU
공식 코드https://github.com/David3384/GI_2019
공식 코드https://github.com/DavidHason/ser
공식 코드Leveraged Mel spectrograms using Harmonic and Percussive Components in Speech Emotion Recognition
https://github.com/DeLightCMU/PSA
공식 코드Polarized Self-Attention: Towards High-quality Pixel-wise Regression
https://github.com/Denys88/rl_games
공식 코드Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
https://github.com/Dianezzy/ParaLip
공식 코드https://github.com/Ding-Liu/DeepDenoising
공식 코드Connecting Image Denoising and High-Level Vision Tasks via Deep Learning
https://github.com/DmitryUlyanov/AGE
공식 코드https://github.com/Duankaiwen/CPNDet
공식 코드Corner Proposal Network for Anchor-free, Two-stage Object Detection
https://github.com/DudleyLab/widefield2confocal
공식 코드Enhancing high-content imaging for studying microtubule networks at large-scale
https://github.com/EIHW/EmoNet
공식 코드EmoNet: A Transfer Learning Framework for Multi-Corpus Speech Emotion Recognition
https://github.com/EMOsuperb/EMO-SUPERB-submission
공식 코드https://github.com/EducationalTestingService/CATS
공식 코드Two-Level Transformer and Auxiliary Coherence Modeling for Improved Text Segmentation
https://github.com/FACEGOOD/FACEGOOD-Audio2Face
공식 코드High-Quality Real Time Facial Capture Based on Single Camera
https://github.com/FDChongli/TwoWaysToImproveCSC
공식 코드Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models
https://github.com/FangyunWei/SLRT
공식 코드Two-Stream Network for Sign Language Recognition and Translation
https://github.com/FederatedSTAR/FedSTAR
공식 코드Privacy-preserving Speech Emotion Recognition through Semi-Supervised Federated Learning
https://github.com/FrankD/fuser
공식 코드https://github.com/GGiecold/ECLAIR
공식 코드Robust Lineage Reconstruction from High-Dimensional Single-Cell Data
https://github.com/GabrieleLagani/SpikingGrid
공식 코드Assessing Pattern Recognition Performance of Neuronal Cultures through Accurate Simulation
https://github.com/Ghadjeres/VarNet
공식 코드Variation Network: Learning High-level Attributes for Controlled Input Manipulation
https://github.com/Gordon-Guojun-Zhang/UAI-2019
공식 코드https://github.com/HIIT/knowledge-elicitation-for-linear-regression
공식 코드Knowledge Elicitation via Sequential Probabilistic Inference for High-Dimensional Prediction
https://github.com/HKUST-NISL/GEDDnet
공식 코드Towards High Performance Low Complexity Calibration in Appearance Based Gaze Estimation
https://github.com/HLTCHKUST/covid19-misinfo-data
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.