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://bitbucket.org/AnatoliyBabic/swi-scisports-2018

공식 코드

SciSports: Learning football kinematics through two-dimensional tracking data

https://bitbucket.org/chhshen/fasthash

공식 코드

Fast Supervised Hashing with Decision Trees for High-Dimensional Data

https://bitbucket.org/georgiou/emotions_as_primitives_towards_behavior_understanding

공식 코드

Linking emotions to behaviors through deep transfer learning

https://bitbucket.org/uwtppml/idash2019

공식 코드

High Performance Logistic Regression for Privacy-Preserving Genome Analysis

https://bitbucket.org/wtstephe/sparse_appx_cv

공식 코드

Approximate Cross-Validation in High Dimensions with Guarantees

https://code.engineering.queensu.ca/pritam/SSL-ECG

공식 코드

Self-supervised Learning for ECG-based Emotion Recognition

https://git.noc.ruhr-uni-bochum.de/vepabm/invano-public

공식 코드

The effects of incentives, social norms, and employees' values on work performance

https://git.skewed.de/count0/graph-tool

공식 코드

Hierarchical Block Structures and High-resolution Model Selection in Large Networks

https://github.com/1Konny/idgan

공식 코드

High-Fidelity Synthesis with Disentangled Representation

https://github.com/1adrianb/expert-binary-networks

공식 코드

High-Capacity Expert Binary Networks

https://github.com/1adrianb/unsupervised-face-representation

공식 코드

Pre-training strategies and datasets for facial representation learning

https://github.com/201528014227051/sged

공식 코드

SGED: A Benchmark dataset for Performance Evaluation of Spiking Gesture Emotion Recognition

https://github.com/201983290498/lddu_mmer

공식 코드

Latent Distribution Decoupling: A Probabilistic Framework for Uncertainty-Aware Multimodal Emotion Recognition

https://github.com/24davidhuang/emotion-qwen

공식 코드

Emotion-Qwen: Training Hybrid Experts for Unified Emotion and General Vision-Language Understanding

https://github.com/601175936/homi

공식 코드

Multi-label Classification with High-rank and High-order Label Correlations

https://github.com/935963004/labram

공식 코드

Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI

https://github.com/953206211/feallm

공식 코드

FEALLM: Advancing Facial Emotion Analysis in Multimodal Large Language Models with Emotional Synergy and Reasoning

https://github.com/95anantsingh/decoding-emotions

공식 코드

Decoding Emotions: A comprehensive Multilingual Study of Speech Models for Speech Emotion Recognition

https://github.com/AClon42/two-sample-anisotropic

공식 코드

Two-sample Statistics Based on Anisotropic Kernels

https://github.com/AI-Unicamp/interspeech_ser

공식 코드

Improving Speech Emotion Recognition Through Cross Modal Attention Alignment and Balanced Stacking Model

https://github.com/AIM3-RUC/ABAW4

공식 코드

Multi-Task Learning Framework for Emotion Recognition in-the-wild

https://github.com/AMAAI-Lab/Music2Emotion

공식 코드

Towards Unified Music Emotion Recognition across Dimensional and Categorical Models

https://github.com/AMICI-dev/AMICI

공식 코드

AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation Models

https://github.com/ANTsX/ANTsR

공식 코드

Interpretable, similarity-driven multi-view embeddings from high-dimensional biomedical data

https://github.com/ARBML/Calliar

공식 코드

Calliar: An Online Handwritten Dataset for Arabic Calligraphy

https://github.com/ASolitaryMan/Foal-Net

공식 코드

Enhancing Modal Fusion by Alignment and Label Matching for Multimodal Emotion Recognition

https://github.com/ATriantafyllopoulos/enrollment-personalization

공식 코드

Enrolment-based personalisation for improving individual-level fairness in speech emotion recognition

https://github.com/ATriantafyllopoulos/is24-interspeech09-ser-revisited

공식 코드

INTERSPEECH 2009 Emotion Challenge Revisited: Benchmarking 15 Years of Progress in Speech Emotion Recognition

https://github.com/AZYoung233/MSE-Adapter

공식 코드

MSE-Adapter: A Lightweight Plugin Endowing LLMs with the Capability to Perform Multimodal Sentiment Analysis and Emotion Recognition

https://github.com/Aaditya-Singh/E2E-ECPE

공식 코드

An End-to-End Network for Emotion-Cause Pair Extraction

https://github.com/Adam1679/ConnectE

공식 코드

Connecting Embeddings for Knowledge Graph Entity Typing

https://github.com/AlanZhang1995/TwoStreamSR

공식 코드

Two-Stream Action Recognition-Oriented Video Super-Resolution

https://github.com/Alena-Xinran/MaTAV

공식 코드

Mamba-Enhanced Text-Audio-Video Alignment Network for Emotion Recognition in Conversations

https://github.com/Alibaba-MIIL/CobBO

공식 코드

CobBO: Coordinate Backoff Bayesian Optimization with Two-Stage Kernels

https://github.com/AmirAbaskohi/Persian-Emotion-Detection-using-ParsBERT-and-Imbalanced-Data-Handling-Approaches

공식 코드

Persian Emotion Detection using ParsBERT and Imbalanced Data Handling Approaches

https://github.com/AmirSh15/Compact_SER

공식 코드

Compact Graph Architecture for Speech Emotion Recognition

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