Papers with Code 아카이브

Emotion Recognition

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

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/meiyor/SincNet-for-Autism-EEG-based-Emotion-Recognition

공식 코드

Interpretable SincNet-based Deep Learning for Emotion Recognition from EEG brain activity

https://github.com/meiyor/deep-learning-emotion-decoding-using-eeg-data-from-autism-individuals

공식 코드

Evaluation of Interpretability for Deep Learning algorithms in EEG Emotion Recognition: A case study in Autism

https://github.com/memx-research/emoship

공식 코드

Do Smart Glasses Dream of Sentimental Visions? Deep Emotionship Analysis for Eyewear Devices

https://github.com/meyerscetbon/L1_test

공식 코드

Comparing distributions: $\ell_1$ geometry improves kernel two-sample testing

https://github.com/meyerscetbon/l1_two_sample_test

공식 코드

Comparing distributions: $\ell_1$ geometry improves kernel two-sample testing

https://github.com/mgiant/2p-gcn

공식 코드

Two-person Graph Convolutional Network for Skeleton-based Human Interaction Recognition

https://github.com/miccunifi/nefer

공식 코드

Neuromorphic Event-based Facial Expression Recognition

https://github.com/michaeltrs/deepsatmodels

공식 코드

Context-self contrastive pretraining for crop type semantic segmentation

https://github.com/michelecafagna26/hl-dataset

공식 코드

HL Dataset: Visually-grounded Description of Scenes, Actions and Rationales

https://github.com/michielbusschaert/heterogeneous_fba

공식 코드

Modeling Cell Size Distribution with Heterogeneous Flux Balance Analysis

https://github.com/mickeystroller/etypeclus

공식 코드

Corpus-based Open-Domain Event Type Induction

https://github.com/microsoft/EDDI

공식 코드

EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE

https://github.com/microsoft/StyleSwin

공식 코드

StyleSwin: Transformer-based GAN for High-resolution Image Generation

https://github.com/microsoft/VAEM

공식 코드

VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data

https://github.com/microsoft/arr-grasp-type-recognition

공식 코드

Text-driven object affordance for guiding grasp-type recognition in multimodal robot teaching

https://github.com/microsoft/fadtk

공식 코드

Addressing Emotion Bias in Music Emotion Recognition and Generation with Frechet Audio Distance

https://github.com/microsoft/msrflute

공식 코드

FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations

https://github.com/microsoft/xpretrain

공식 코드

Advancing High-Resolution Video-Language Representation with Large-Scale Video Transcriptions

https://github.com/midas-research/audino

공식 코드

audino: A Modern Annotation Tool for Audio and Speech

https://github.com/midobal/mthd

공식 코드

Two Demonstrations of the Machine Translation Applications to Historical Documents

https://github.com/milanlproc/emotion_analysis_survey

공식 코드

Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directions

https://github.com/milanlproc/emotion_gendered_stereotypes

공식 코드

Angry Men, Sad Women: Large Language Models Reflect Gendered Stereotypes in Emotion Attribution

https://github.com/miltoncandela/neurohumanities-lab

공식 코드

Real-time EEG-based Emotion Recognition Model using Principal Component Analysis and Tree-based Models for Neurohumanities

https://github.com/mingyan08/RPCA_Rank_Bound

공식 코드

Fast algorithms for robust principal component analysis with an upper bound on the rank

https://github.com/minhnhatvt/glamor-net

공식 코드

Global-Local Attention for Emotion Recognition

https://github.com/minimalparts/nonce2vec

공식 코드

High-risk learning: acquiring new word vectors from tiny data

https://github.com/mintelligence-group/mmemorec

공식 코드

VISTANet: VIsual Spoken Textual Additive Net for Interpretable Multimodal Emotion Recognition

https://github.com/mintelligence-group/speechimg_emorec

공식 코드

Interpretable Multimodal Emotion Recognition using Hybrid Fusion of Speech and Image Data

https://github.com/mips-colt/mer-mce

공식 코드

MIPS at SemEval-2024 Task 3: Multimodal Emotion-Cause Pair Extraction in Conversations with Multimodal Language Models

https://github.com/mirah-official/empathetic-chatbot-asem

공식 코드

ASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and Emotion Modeling

https://github.com/mitmedialab/sherlock-project

공식 코드

Sherlock: A Deep Learning Approach to Semantic Data Type Detection

https://github.com/mkirchler/deep-2-sample-test

공식 코드

Two-sample Testing Using Deep Learning

https://github.com/mmakiuchi/multimodal_emotion_recognition

공식 코드

Multimodal Emotion Recognition with High-level Speech and Text Features

https://github.com/mmezache/HFFTest

공식 코드

Testing for high frequency features in a noisy signal

https://github.com/mnecipkurt/pami20

공식 코드

Real-Time Nonparametric Anomaly Detection in High-Dimensional Settings

https://github.com/modelscope/adaseq

공식 코드

Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field

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