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Emotion Recognition

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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/OSU-CMR/HICU

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High-dimensional Fast Convolutional Framework (HICU) for Calibrationless MRI

https://github.com/OmarHagrass/Spectral-regularized-two-sample-test

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Spectral Regularized Kernel Two-Sample Tests

https://github.com/OmarMohammed88/AR-Emotion-Recognition

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Arabic Speech Emotion Recognition Employing Wav2vec2.0 and HuBERT Based on BAVED Dataset

https://github.com/P2333/Rectified-Rejection

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Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart

https://github.com/PaddlePaddle/PaddleSeg

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PaddleSeg: A High-Efficient Development Toolkit for Image Segmentation

https://github.com/PaddlePaddle/models

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Towards Conversational Recommendation over Multi-Type Dialogs

https://github.com/Panern/HMvC

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High-order Multi-view Clustering for Generic Data

https://github.com/PanosAntoniadis/NTUA-ABAW2021

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An audiovisual and contextual approach for categorical and continuous emotion recognition in-the-wild

https://github.com/Partaourides/SERN

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A Self-Attentive Emotion Recognition Network

https://github.com/Pea-Shooter/ADNet

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ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging

https://github.com/Peihao-Xiang/MTCAE-DFER

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MTCAE-DFER: Multi-Task Cascaded Autoencoder for Dynamic Facial Expression Recognition

https://github.com/Peihao-Xiang/MultiMAE-DFER

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MultiMAE-DER: Multimodal Masked Autoencoder for Dynamic Emotion Recognition

https://github.com/PeterL1n/BackgroundMattingV2

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Real-Time High-Resolution Background Matting

https://github.com/PeterL1n/RobustVideoMatting

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Robust High-Resolution Video Matting with Temporal Guidance

https://github.com/PierreMarion23/two-timescale-nn

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Leveraging the two timescale regime to demonstrate convergence of neural networks

https://github.com/PoloWlg/Joint-Multimodal-Transformer-6th-ABAW

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Joint Multimodal Transformer for Emotion Recognition in the Wild

https://github.com/PouyaREZ/GAN_GA

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Augmenting High-dimensional Nonlinear Optimization with Conditional GANs

https://github.com/Pythongoras/ConeProjectedPowerIter

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Non-Sparse PCA in High Dimensions via Cone Projected Power Iteration

https://github.com/QB3/sparse-ho

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Implicit differentiation of Lasso-type models for hyperparameter optimization

https://github.com/Qsingle/MegVision

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SuperVessel: Segmenting High-resolution Vessel from Low-resolution Retinal Image

https://github.com/QuantLet/CCID

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Rise of the Machines? Intraday High-Frequency Trading Patterns of Cryptocurrencies

https://github.com/R00Kie-Liu/TA2N

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TA2N: Two-Stage Action Alignment Network for Few-shot Action Recognition

https://github.com/RQ-Wu/RIDCP_dehazing

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RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priors

https://github.com/Raminghorbanii/Self-Supervised-PPG-Representation-Learning-Shows-High-Inter-Subject-Variability

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Self-Supervised PPG Representation Learning Shows High Inter-Subject Variability

https://github.com/RanyaJumah/PP_Speech_Analysis

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Emotionless: Privacy-Preserving Speech Analysis for Voice Assistants

https://github.com/RaymZhang/TS_Combinatorial_Semi_Bandits_Curse

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On the Suboptimality of Thompson Sampling in High Dimensions

https://github.com/Rhitabrat/youtube-comments-categorization

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Classifying YouTube Comments Based on Sentiment and Type of Sentence

https://github.com/RichardMoot/GrailLight

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The Grail theorem prover: Type theory for syntax and semantics

https://github.com/RixonC/DINGO

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DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization

https://github.com/Rongjiehuang/FastDiff

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FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis

https://github.com/Rongjiehuang/ProDiff

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ProDiff: Progressive Fast Diffusion Model For High-Quality Text-to-Speech

https://github.com/RuiLiFeng/Deep-Generative-Projection

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Weakly Supervised High-Fidelity Clothing Model Generation

https://github.com/SCCAF/sccaf

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Putative cell type discovery from single-cell gene expression data

https://github.com/SPEECHCOG/PFML

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PFML: Self-Supervised Learning of Time-Series Data Without Representation Collapse

https://github.com/SPEECHCOG/cpc_pytorch

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Analysis of Self-Supervised Learning and Dimensionality Reduction Methods in Clustering-Based Active Learning for Speech Emotion Recognition

https://github.com/Safe-DS/DSL

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Safe-DS: A Domain Specific Language to Make Data Science Safe

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