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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/seymayucer/VAEs

Generating Diverse High-Fidelity Images with VQ-VAE-2

https://github.com/sgamezrdo/anomaly-aoutoencoding

Anomaly Detection using Autoencoders in High Performance Computing Systems

https://github.com/sh951011/Attention-Implementation

Attention Is All You Need

https://github.com/shagunsodhani/CNN-Sentence-Classifier

Convolutional Neural Networks for Sentence Classification

https://github.com/shahabi8/Deep-Reinforcement-Learning

Continuous control with deep reinforcement learning

https://github.com/shahin-01/vqa-ad

Continuous control with deep reinforcement learning

https://github.com/shahrukhx01/ocr-test

Attention Is All You Need

https://github.com/shaikhzhas/bert

Attention Is All You Need

https://github.com/shakedzy/warehouse

Continuous control with deep reinforcement learning

https://github.com/shanzhenren/AFET

Context-Dependent Fine-Grained Entity Type Tagging

https://github.com/shao-chi/ImageCaption

Attention Is All You Need

https://github.com/shaoyent/multimodal-elmo

Multimodal Embeddings from Language Models

https://github.com/shawnyxiao/textclassification-keras

Bag of Tricks for Efficient Text Classification

https://github.com/shehrum/RL_Continous-Control

Continuous control with deep reinforcement learning

https://github.com/sheng-z/figet

Context-Dependent Fine-Grained Entity Type Tagging

https://github.com/sherinaseri/bert

Attention Is All You Need

https://github.com/sherkhan15/distarctor-Detection_Eyes_Emotions_CI

Student Engagement Detection Using Emotion Analysis, Eye Tracking and Head Movement with Machine Learning

https://github.com/shifwang/irf

Iterative Random Forests to detect predictive and stable high-order interactions

https://github.com/shijianjian/HRNet_Keras

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/shmilyo/Low-Cost-Edge-Sensing-for-High-Quality-Demosaicking

Low Cost Edge Sensing for High Quality Demosaicking

https://github.com/shouvikcirca/Transformer

Attention Is All You Need

https://github.com/shouvikcirca/Transformer_MiniProject

Attention Is All You Need

https://github.com/showlab/Tune-A-Video

High-Resolution Image Synthesis with Latent Diffusion Models

https://github.com/showlab/loveu-tgve-2023

High-Resolution Image Synthesis with Latent Diffusion Models

https://github.com/shreyash8/English_to_Hindi_NMT

Attention Is All You Need

https://github.com/shreyashankar/gpt3-sandbox

Attention Is All You Need

https://github.com/shreyesss/PPO-implementation-keras-tensorflow

High-Dimensional Continuous Control Using Generalized Advantage Estimation

https://github.com/shrookehab/Body-Language-and-Emotion-Recognition

Context Based Emotion Recognition using EMOTIC Dataset

https://github.com/shuaishuaij/Machine-Translation

Attention Is All You Need

https://github.com/shunsukesaito/PIFu

PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization

https://github.com/shuuchen/HRNet

Deep High-Resolution Representation Learning for Visual Recognition

https://github.com/shwoo93/minkowskiengine

High-dimensional Convolutional Networks for Geometric Pattern Recognition

https://github.com/shyam-skk/Parallel_CNN_Transfromer

Attention Is All You Need

https://github.com/shyammarjit/EEG-Emotion-Recognition

https://github.com/siavashshams/ssamba

SSAST: Self-Supervised Audio Spectrogram Transformer

https://github.com/siddinc/monocular_depth_estimation

High Quality Monocular Depth Estimation via Transfer Learning

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