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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/IDKiro/MCT

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Multi-Curve Translator for High-Resolution Photorealistic Image Translation

https://github.com/IIP-Sogang/speech-pooling-benchmark

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Unsupervised Speech Representation Pooling Using Vector Quantization

https://github.com/IdePHICS/GaussMixtureProject

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Learning Gaussian Mixtures with Generalised Linear Models: Precise Asymptotics in High-dimensions

https://github.com/ImagingLab/ICIAR2018

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Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification

https://github.com/InterDigitalInc/HRFAE

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High Resolution Face Age Editing

https://github.com/IouJenLiu/HTS-RL

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High-Throughput Synchronous Deep RL

https://github.com/IssamLaradji/cownter_strike

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Counting Cows: Tracking Illegal Cattle Ranching From High-Resolution Satellite Imagery

https://github.com/ItaySafran/OneLayerGDconvergence

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Spurious Local Minima are Common in Two-Layer ReLU Neural Networks

https://github.com/IvonaTau/emotionaldan

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Classifying and Visualizing Emotions with Emotional DAN

https://github.com/JULIELab/EmoBank

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EmoBank: Studying the Impact of Annotation Perspective and Representation Format on Dimensional Emotion Analysis

https://github.com/JULIELab/MEmoLon

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Learning and Evaluating Emotion Lexicons for 91 Languages

https://github.com/JackYFL/EmoLA

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Facial Affective Behavior Analysis with Instruction Tuning

https://github.com/Jan10e/Broek_Neuron-Excitability_2020

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Generalisation of neuronal excitability allows for the identification of an excitability change parameter that links to an experimentally measurable value

https://github.com/Jee-King/TSAN

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A Two-Stage Attentive Network for Single Image Super-Resolution

https://github.com/Jencke/binaural-detection-model

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A hemispheric two-channel code accounts for binaural unmasking in humans

https://github.com/Jiaxin-Ye/TIM-Net_SER

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Temporal Modeling Matters: A Novel Temporal Emotional Modeling Approach for Speech Emotion Recognition

https://github.com/JiayuanDing100/Position-Bias

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An Experimental Study of The Effects of Position Bias on Emotion CauseExtraction

https://github.com/JinchaoLove/AffectiveVocalBurstRecognition

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A Hierarchical Regression Chain Framework for Affective Vocal Burst Recognition

https://github.com/JmfanBU/AdvIBP

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Adversarial Training and Provable Robustness: A Tale of Two Objectives

https://github.com/Jpvmello/type-identification-mri-sequences

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Deep Learning-based Type Identification of Volumetric MRI Sequences

https://github.com/JuHo-Jung/HiQuE

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HiQuE: Hierarchical Question Embedding Network for Multimodal Depression Detection

https://github.com/JuliaSymbolics/SymbolicUtils.jl

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High-performance symbolic-numerics via multiple dispatch

https://github.com/JumanaFM/emoviz

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Speech-Based Emotion Recognition using Neural Networks and Information Visualization

https://github.com/Justin1904/Low-rank-Multimodal-Fusion

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Efficient Low-rank Multimodal Fusion with Modality-Specific Factors

https://github.com/KMnP/vise

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Exploring Visual Engagement Signals for Representation Learning

https://github.com/Kaist-ICLab/K-EmoCon_SupplementaryCodes

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K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations

https://github.com/Kingsford-Group/vsbo

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Computationally Efficient High-Dimensional Bayesian Optimization via Variable Selection

https://github.com/KisuYang/EmotionX-KU

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EmotionX-KU: BERT-Max based Contextual Emotion Classifier

https://github.com/KiveeDong/CentripetalNet

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CentripetalNet: Pursuing High-quality Keypoint Pairs for Object Detection

https://github.com/Kohei-Matsuura/Non-parallel-VC-on-Mboshi

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Generative Adversarial Training Data Adaptation for Very Low-resource Automatic Speech Recognition

https://github.com/LCS2-IIITD/MOSES

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Explaining (Sarcastic) Utterances to Enhance Affect Understanding in Multimodal Dialogues

https://github.com/LIN-SHANG/InstructERC

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InstructERC: Reforming Emotion Recognition in Conversation with Multi-task Retrieval-Augmented Large Language Models

https://github.com/LLNL/fpp

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Function Preserving Projection for Scalable Exploration of High-Dimensional Data

https://github.com/LaVieEnRoseSMZ/OQA

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Once Quantization-Aware Training: High Performance Extremely Low-bit Architecture Search

https://github.com/Lemma1/NGSIM-interface

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High-Resolution Traffic Sensing with Autonomous Vehicles

https://github.com/LeyuChen/L0-ERM

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High Dimensional Classification through $\ell_0$-Penalized Empirical Risk Minimization

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