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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/Salingo/CTF-Net

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Consistent Two-Flow Network for Tele-Registration of Point Clouds

https://github.com/SaschaHornauer/Batvision

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BatVision: Learning to See 3D Spatial Layout with Two Ears

https://github.com/SenticNet/conv-emotion

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DialogueRNN: An Attentive RNN for Emotion Detection in Conversations

https://github.com/SeyedMuhammadHosseinMousavi/Synthetic-Data-Generation-by-Supervised-Neural-Gas-Network

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Synthetic Data Generation by Supervised Neural Gas Network for Physiological Emotion Recognition Data

https://github.com/SeyedMuhammadHosseinMousavi/Synthetic-Data-Generation-of-Body-Motion-Data-by-Neural-Gas-Network-for-Emotion-Recognition

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Synthetic Data Generation of Body Motion Data by Neural Gas Network for Emotion Recognition

https://github.com/ShangtongZhang/DeepRL

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Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation

https://github.com/ShinyWang33/eSL-Net

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Event Enhanced High-Quality Image Recovery

https://github.com/ShuyangCao/open-ended_question_ontology

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Controllable Open-ended Question Generation with A New Question Type Ontology

https://github.com/SkBlaz/PropStar

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Propositionalization and Embeddings: Two Sides of the Same Coin

https://github.com/SkBlaz/reliefe

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ReliefE: Feature Ranking in High-dimensional Spaces via Manifold Embeddings

https://github.com/SocratesNFR/Evolving-spiking-neuron-cellular-automata-and-networks-to-emulate-in-vitro-neuronal-activity

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Evolving spiking neuron cellular automata and networks to emulate in vitro neuronal activity

https://github.com/Sootung/RAMer

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RAMer: Reconstruction-based Adversarial Model for Multi-party Multi-modal Multi-label Emotion Recognition

https://github.com/SoumiDas/HOST-CP

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Finding High-Value Training Data Subset through Differentiable Convex Programming

https://github.com/Soumyabrata/HDR-cloud-segmentation

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High-Dynamic-Range Imaging for Cloud Segmentation

https://github.com/SparseGridsForDynamicEcon/HDMR

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High-Dimensional Dynamic Stochastic Model Representation

https://github.com/SpectData/MONAH

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MONAH: Multi-Modal Narratives for Humans to analyze conversations

https://github.com/Spencerfar/djin-aging

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Interpretable machine learning for high-dimensional trajectories of aging health

https://github.com/Stalence/NeuralExt

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Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions

https://github.com/StanfordSocialNeuroscienceLab/SEND

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Modeling emotion in complex stories: the Stanford Emotional Narratives Dataset

https://github.com/Stefan-Pasch/CultureBERT

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CultureBERT: Measuring Corporate Culture With Transformer-Based Language Models

https://github.com/SysCV/transfiner

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Mask Transfiner for High-Quality Instance Segmentation

https://github.com/TIBHannover/VisE

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Ontology-driven Event Type Classification in Images

https://github.com/TLouf/words-use

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American cultural regions mapped through the lexical analysis of social media

https://github.com/TUM-AIMED/MoNet

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Efficient, high-performance pancreatic segmentation using multi-scale feature extraction

https://github.com/TUM-LMF/BreizhCrops

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BreizhCrops: A Time Series Dataset for Crop Type Mapping

https://github.com/TamirBendory/high-dimensional-mra-bounds

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Multi-reference alignment in high dimensions: sample complexity and phase transition

https://github.com/Tengfei-Wang/HFGI

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High-Fidelity GAN Inversion for Image Attribute Editing

https://github.com/Tju-AI/two-stage-labeling-for-the-sentiment-orientations

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$ρ$-hot Lexicon Embedding-based Two-level LSTM for Sentiment Analysis

https://github.com/TobiasGruening/ARU-Net

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A Two-Stage Method for Text Line Detection in Historical Documents

https://github.com/TraceyZhong/EBPCA

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Empirical Bayes PCA in high dimensions

https://github.com/Trustworthy-AI-Group/DHF

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Diversifying the High-level Features for better Adversarial Transferability

https://github.com/TurboFreeze/dp-robust-filter

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Designing Differentially Private Estimators in High Dimensions

https://github.com/UNHSAILLab/SentimentalLIAR

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Sentimental LIAR: Extended Corpus and Deep Learning Models for Fake Claim Classification

https://github.com/Vincent-ZHQ/MSER

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Large Language Models Meet Contrastive Learning: Zero-Shot Emotion Recognition Across Languages

https://github.com/Vivianstats/TUBE

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Bridging Cost-sensitive and Neyman-Pearson Paradigms for Asymmetric Binary Classification

https://github.com/VoiceBeer/MS-MDA

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MS-MDA: Multisource Marginal Distribution Adaptation for Cross-subject and Cross-session EEG Emotion Recognition

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