Papers with Code 아카이브
Emotion Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/dipteshdas/cp-shim
공식 코드A Confidence Machine for Sparse High-Order Interaction Model
https://github.com/djiajunustc/Voxel-R-CNN
공식 코드Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection
https://github.com/dl4sits/BreizhCrops
공식 코드https://github.com/dlion168/Listen-and-Speak-Fairly
공식 코드Listen and Speak Fairly: A Study on Semantic Gender Bias in Speech Integrated Large Language Models
https://github.com/dobriban/dist_ridge
공식 코드WONDER: Weighted one-shot distributed ridge regression in high dimensions
https://github.com/dobriban/high-dim-risk-experiments
공식 코드High-Dimensional Asymptotics of Prediction: Ridge Regression and Classification
https://github.com/doerlbh/mentalRL
공식 코드Split Q Learning: Reinforcement Learning with Two-Stream Rewards
https://github.com/doinab/constellation-lines
공식 코드https://github.com/domenicrosati/citation-function-corpus-lingustics
공식 코드Citations are not opinions: a corpus linguistics approach to understanding how citations are made
https://github.com/donydchen/causal_emotion
공식 코드Towards Unbiased Visual Emotion Recognition via Causal Intervention
https://github.com/dougpsg/hif_vegetation_data
공식 코드VeHIF: An Accessible Vegetation High-Impedance Fault Data Set Format
https://github.com/dpiras/cosmopower-jax
공식 코드CosmoPower-JAX: high-dimensional Bayesian inference with differentiable cosmological emulators
https://github.com/dppalomar/highOrderPortfolios
공식 코드Solving High-Order Portfolios via Successive Convex Approximation Algorithms
https://github.com/dprn/srLHE
공식 코드A cortical-inspired sub-Riemannian model for Poggendorff-type visual illusions
https://github.com/drckf/mlreview_notebooks
공식 코드A high-bias, low-variance introduction to Machine Learning for physicists
https://github.com/dsciitism/SemEval-2020-Task-8
공식 코드DSC IIT-ISM at SemEval-2020 Task 8: Bi-Fusion Techniques for Deep Meme Emotion Analysis
https://github.com/dselsam/neurocore-public
공식 코드Guiding High-Performance SAT Solvers with Unsat-Core Predictions
https://github.com/dsouzadaniel/long_tail
공식 코드https://github.com/dual-grp/DONE
공식 코드DONE: Distributed Approximate Newton-type Method for Federated Edge Learning
https://github.com/duanyiqun/ctnas-eeg
공식 코드Cross Task Neural Architecture Search for EEG Signal Classifications
https://github.com/ducha-aiki/mods
공식 코드https://github.com/dvlab-research/Entity
공식 코드https://github.com/dw1010/intaghand
공식 코드Interacting Attention Graph for Single Image Two-Hand Reconstruction
https://github.com/dynamic-superb/dynamic-superb
공식 코드https://github.com/dzachariah/two-groups-data
공식 코드https://github.com/eeskimez/emotalkingface
공식 코드Speech Driven Talking Face Generation from a Single Image and an Emotion Condition
https://github.com/eezkni/lgfm
공식 코드High Dynamic Range Image Quality Assessment Based on Frequency Disparity
https://github.com/eihw/muse-2023
공식 코드https://github.com/eihw/muse2022
공식 코드The MuSe 2022 Multimodal Sentiment Analysis Challenge: Humor, Emotional Reactions, and Stress
https://github.com/ekochmar/MWE-CWI
공식 코드Detecting Multiword Expression Type Helps Lexical Complexity Assessment
https://github.com/eleyng/cooperative_planner
공식 코드It Takes Two: Learning to Plan for Human-Robot Cooperative Carrying
https://github.com/eleyng/table-carrying-ai
공식 코드It Takes Two: Learning to Plan for Human-Robot Cooperative Carrying
https://github.com/elliottwu/DeepHDR
공식 코드Deep High Dynamic Range Imaging with Large Foreground Motions
https://github.com/emanuele/jstsp2015
공식 코드https://github.com/emo-box/emobox
공식 코드EmoBox: Multilingual Multi-corpus Speech Emotion Recognition Toolkit and Benchmark
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.