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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/LiXinran6/LSDGNN_ICL

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Long-Short Distance Graph Neural Networks and Improved Curriculum Learning for Emotion Recognition in Conversation

https://github.com/Lili-Zheng-stat/TTOI

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Optimal High-order Tensor SVD via Tensor-Train Orthogonal Iteration

https://github.com/LittlePey/SFD

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Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion

https://github.com/Littleor/Personalized-DMER

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Personalized Dynamic Music Emotion Recognition with Dual-Scale Attention-Based Meta-Learning

https://github.com/LorenzoRimella/GraphFilter-GraphSmoother

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Exploiting locality in high-dimensional factorial hidden Markov models

https://github.com/Luca96/affine-parametric-networks

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Improving Parametric Neural Networks for High-Energy Physics (and Beyond)

https://github.com/LuminosoInsight/conceptnet-vector-ensemble

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An Ensemble Method to Produce High-Quality Word Embeddings (2016)

https://github.com/LyuJ1998/HandTailor

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HandTailor: Towards High-Precision Monocular 3D Hand Recovery

https://github.com/M4t1ss/sentiment-analysis-toolkit

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Fragmented and Valuable: Following Sentiment Changes in Food Tweets

https://github.com/MSA-LMC/PhysioSync

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PhysioSync: Temporal and Cross-Modal Contrastive Learning Inspired by Physiological Synchronization for EEG-Based Emotion Recognition

https://github.com/MSA-LMC/VAEmo

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VAEmo: Efficient Representation Learning for Visual-Audio Emotion with Knowledge Injection

https://github.com/MahmoudRabea13/IdentiFace

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IdentiFace : A VGG Based Multimodal Facial Biometric System

https://github.com/MarekKowalski/HoloFace

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HoloFace: Augmenting Human-to-Human Interactions on HoloLens

https://github.com/MartinPernus/MaskFaceGAN

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High Resolution Face Editing with Masked GAN Latent Code Optimization

https://github.com/MarvinLvn/voice-type-classifier

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An open-source voice type classifier for child-centered daylong recordings

https://github.com/MatanCohen1/Transformaly

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Transformaly -- Two (Feature Spaces) Are Better Than One

https://github.com/Mathux/FROT

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Feature Robust Optimal Transport for High-dimensional Data

https://github.com/Maxwe11y/BiERU

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BiERU: Bidirectional Emotional Recurrent Unit for Conversational Sentiment Analysis

https://github.com/Microsoft/human-pose-estimation.pytorch

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Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/MihawkHu/DCASE2020_task1

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A Two-Stage Approach to Device-Robust Acoustic Scene Classification

https://github.com/MrVPlusOne/LambdaNet

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LambdaNet: Probabilistic Type Inference using Graph Neural Networks

https://github.com/MungoMeng/Panorama-OpticalFlow

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High-quality Panorama Stitching based on Asymmetric Bidirectional Optical Flow

https://github.com/MungoMeng/Spiking-Inception

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High-parallelism Inception-like Spiking Neural Networks for Unsupervised Feature Learning

https://github.com/NFFT/ANOVAapprox

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Interpretable Approximation of High-Dimensional Data

https://github.com/NFFT/AttributeRankingExamples

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Interpretable Approximation of High-Dimensional Data

https://github.com/NUSTM/ECPE

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Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts

https://github.com/NUSTM/PAEDGL

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From Independent Prediction to Re-ordered Prediction: Integrating Relative Position and Global Label Information to Emotion Cause Identification

https://github.com/NUSTM/RTHN

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RTHN: A RNN-Transformer Hierarchical Network for Emotion Cause Extraction

https://github.com/NVIDIA/pix2pixHD

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High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

https://github.com/NVlabs/nvdiffrast

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Modular Primitives for High-Performance Differentiable Rendering

https://github.com/NVlabs/two-shot-brdf-shape

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Two-shot Spatially-varying BRDF and Shape Estimation

https://github.com/Nakols/HFformerV2

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Exploring the Advantages of Transformers for High-Frequency Trading

https://github.com/NeurEXT/NEXT-learning-to-plan/blob/master/main.ipynb

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Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees

https://github.com/NiMlr/High-Dim-ES-RL

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Challenges in High-dimensional Reinforcement Learning with Evolution Strategies

https://github.com/NiallJeffrey/MomentNetworks

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Solving high-dimensional parameter inference: marginal posterior densities & Moment Networks

https://github.com/NoemieJaquier/GaBOtorch

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High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.