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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/brandonserna/sentiment2emoji

EmoTxt: A Toolkit for Emotion Recognition from Text

https://github.com/brandontrabucco/scaled_dot_product_attention

Attention Is All You Need

https://github.com/brianyan918/CNN-for-Sentence-Classification

Convolutional Neural Networks for Sentence Classification

https://github.com/brightlaboratory/polydl

PolyDL: Polyhedral Optimizations for Creation of High Performance DL primitives

https://github.com/brightmart/ai_law

Convolutional Neural Networks for Sentence Classification

https://github.com/brightmart/bert_customized

Attention Is All You Need

https://github.com/brightmart/bert_original

Attention Is All You Need

https://github.com/brightmart/roberta_zh

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/brightmart/text_classification

Bag of Tricks for Efficient Text Classification

https://github.com/broccolik/BERT-NER

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/bryanyzhu/two-stream-pytorch

Towards Good Practices for Very Deep Two-Stream ConvNets

https://github.com/bshantam97/Attention_Based_Networks

Attention Is All You Need

https://github.com/bung87/fastText

Bag of Tricks for Efficient Text Classification

https://github.com/bupt-ai-cz/hhcl-reid

Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net

https://github.com/buzkent86/EnKCF_Tracker

EnKCF: Ensemble of Kernelized Correlation Filters for High-Speed Object Tracking

https://github.com/buzkent86/EnKCF_Tracking_WACV18

EnKCF: Ensemble of Kernelized Correlation Filters for High-Speed Object Tracking

https://github.com/bytedance/neurst

Attention Is All You Need

https://github.com/bzhangGo/zero

Attention Is All You Need

https://github.com/bzz/LangID

Bag of Tricks for Efficient Text Classification

https://github.com/cadia-lvl/fastspeech2

Attention Is All You Need

https://github.com/caesarjuly/reginx/blob/master/trainer/models/final_mlp.py

FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction

https://github.com/cahya-wirawan/indonesian-language-models

Attention Is All You Need

https://github.com/cai-cong/MER25_personality

MDPE: A Multimodal Deception Dataset with Personality and Emotional Characteristics

https://github.com/caiyuanhao1998/MST-plus-plus

HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging

https://github.com/caiyuanhao1998/hdr-gs

HDR-NeRF: High Dynamic Range Neural Radiance Fields

https://github.com/callmefish/NFnet

High-Performance Large-Scale Image Recognition Without Normalization

https://github.com/camel-lab/arabic-gec

Automatic Error Type Annotation for Arabic

https://github.com/camilocarvajalreyes/sfws-stable-diffusion

High-Resolution Image Synthesis with Latent Diffusion Models

https://github.com/camlaedtke/NLP

Attention Is All You Need

https://github.com/candicexxx-droid/PIC16B---GAN-Project-

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

https://github.com/caoyujiALgLM/NLP

Attention Is All You Need

https://github.com/caoyujiALgLM/NLP-progress

Attention Is All You Need

https://github.com/carbonati/fl-zoo

FedDANE: A Federated Newton-Type Method

https://github.com/cedrickchee/pytorch-pretrained-BERT

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/cgaroufis/msspt

AST: Audio Spectrogram Transformer

https://github.com/chail/anyres-gan

Any-resolution Training for High-resolution Image Synthesis

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