https://github.com/zihuazheng/dip
공식 코드DIP: Deep Inverse Patchmatch for High-Resolution Optical Flow
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/ziweimei/pmtest
공식 코드A Heteroskedasticity-Robust Overidentifying Restriction Test with High-Dimensional Covariates
https://github.com/ziyizhang27/dite-hrnet
공식 코드Dite-HRNet: Dynamic Lightweight High-Resolution Network for Human Pose Estimation
https://github.com/ziyuw/rembo
공식 코드High Dimensional Bayesian Optimization via Supervised Dimension Reduction
https://github.com/zjhthu/OANet
공식 코드Learning Two-View Correspondences and Geometry Using Order-Aware Network
https://github.com/zrc007/styleidentitygan
공식 코드Few-shots Portrait Generation with Style Enhancement and Identity Preservation
https://github.com/zsy-9/STNN-DDI
공식 코드STNN-DDI: A Substructure-aware Tensor Neural Network to Predict Drug-Drug Interactions
https://github.com/zxzhao0/c2ser
공식 코드https://github.com/zysxmu/hqss
공식 코드https://github.com/zzd1992/Adversarial-Defense-by-Suppressing-High-Frequencies
공식 코드Adversarial Defense by Suppressing High-frequency Components
https://gitlab.com/compstorylab/contagiograms
공식 코드https://gitlab.com/compstorylab/storywrangler
공식 코드https://gitlab.com/computationalscience/loss-visualization
공식 코드Visualizing high-dimensional loss landscapes with Hessian directions
https://gitlab.com/dlr-dw/type-inference
공식 코드Cross-Domain Evaluation of a Deep Learning-Based Type Inference System
https://gitlab.com/i-flow/i-flow
공식 코드i-flow: High-dimensional Integration and Sampling with Normalizing Flows
https://gitlab.com/luigifreda/3dpatrolling
공식 코드3D Multi-Robot Patrolling with a Two-Level Coordination Strategy
https://gitlab.com/mindfreeze/icip2022
공식 코드Frame-type Sensitive RDO Control for Content-Adaptive-encoding
https://gitlab.com/skalo/baroque-nmt
공식 코드Modeling Baroque Two-Part Counterpoint with Neural Machine Translation
https://zenodo.org/record/8077639
공식 코드Multimodal Multi-User Surface Recognition with the Kernel Two-Sample Test
https://dagshub.com/arnavr.neo/VT-LPR
Vehicle and License Plate Recognition with Novel Dataset for Toll Collection
https://github.com/04RR/SOTA-Vision
https://github.com/0x0f0f0f/Metatheory.jl
https://github.com/163GitHub/AI
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/1wy/bert
https://github.com/2023-MindSpore-1/ms-code-14/tree/main/Transformer
https://github.com/2023-MindSpore-1/ms-code-16/tree/main/ADNet
ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging
https://github.com/2023-MindSpore-1/ms-code-162
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/2023-MindSpore-1/ms-code-210/tree/main/DDAG
https://github.com/2023-MindSpore-1/ms-code-213/tree/main/FaceDetection
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/HMR
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/HRNetW48_cls
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/HRNetW48_seg
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/ibnnet
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/RefineNet
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/tsn
TSNAT: Two-Step Non-Autoregressvie Transformer Models for Speech Recognition
https://github.com/2023-MindSpore-1/ms-code-224/tree/main/ADNet
ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.