Papers with Code 아카이브
Emotion Recognition
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- 평가표
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- 보존된 평가 기록
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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/wtomin/MIMAMO-Net
공식 코드MIMAMO Net: Integrating Micro- and Macro-motion for Video Emotion Recognition
https://github.com/wtomin/Multitask-Emotion-Recognition-with-Incomplete-Labels
공식 코드https://github.com/wuhuikai/GP-GAN
공식 코드https://github.com/wulongfeng/logirec
공식 코드Towards High-Order Complementary Recommendation via Logical Reasoning Network
https://github.com/wustl-cig/CURE
공식 코드Learning Cross-Video Neural Representations for High-Quality Frame Interpolation
https://github.com/wxjiao/AGHMN
공식 코드Real-Time Emotion Recognition via Attention Gated Hierarchical Memory Network
https://github.com/wxjiao/HiGRUs
공식 코드HiGRU: Hierarchical Gated Recurrent Units for Utterance-level Emotion Recognition
https://github.com/wxjiao/Pre-CODE
공식 코드Exploiting Unsupervised Data for Emotion Recognition in Conversations
https://github.com/wyfunique/laf-dbscan
공식 코드Learned Accelerator Framework for Angular-Distance-Based High-Dimensional DBSCAN
https://github.com/wykstc/MERC-main
공식 코드Joyful: Joint Modality Fusion and Graph Contrastive Learning for Multimodal Emotion Recognition
https://github.com/wyu97/InjType
공식 코드https://github.com/wzhouxiff/restoreformer
공식 코드RestoreFormer: High-Quality Blind Face Restoration from Undegraded Key-Value Pairs
https://github.com/xeaver/emotionclip
공식 코드Learning Emotion Representations from Verbal and Nonverbal Communication
https://github.com/xfengnefx/hifiasm-meta
공식 코드Metagenome assembly of high-fidelity long reads with hifiasm-meta
https://github.com/xfold/discoveringandinterpretingconceptualbiases
공식 코드Discovering and Interpreting Biased Concepts in Online Communities
https://github.com/xianghenghe/Improved_StarGAN_Emotional_Voice_Conversion
공식 코드An Improved StarGAN for Emotional Voice Conversion: Enhancing Voice Quality and Data Augmentation
https://github.com/xiaomanluo/ripwPaper
공식 코드Design-Robust Two-Way-Fixed-Effects Regression For Panel Data
https://github.com/xiaoyaoxinyi/m2se
공식 코드EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding
https://github.com/xiaoyuanYi/WMPoetry
공식 코드https://github.com/xinDW/RFGANM
공식 코드High-throughput, high-resolution registration-free generated adversarial network microscopy
https://github.com/xingyizhou/CenterNet2
공식 코드https://github.com/xjtu-eeg/libeer
공식 코드LibEER: A Comprehensive Benchmark and Algorithm Library for EEG-based Emotion Recognition
https://github.com/xmh1011/at-dgnn
공식 코드MEEG and AT-DGNN: Improving EEG Emotion Recognition with Music Introducing and Graph-based Learning
https://github.com/xuecwu/emotions
공식 코드Towards Emotion Analysis in Short-form Videos: A Large-Scale Dataset and Baseline
https://github.com/xufangzhi/logiformer
공식 코드Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoning
https://github.com/xufangzhi/taco
공식 코드https://github.com/xxxxxxxxy/Memotion-Analysis
공식 코드NUAA-QMUL at SemEval-2020 Task 8: Utilizing BERT and DenseNet for Internet Meme Emotion Analysis
https://github.com/xxxxxxxxy/memotion3-SEFusion
공식 코드https://github.com/xycheng/manifold_mmd
공식 코드https://github.com/yaoshuwang/SelNet-Estimation
공식 코드Consistent and Flexible Selectivity Estimation for High-Dimensional Data
https://github.com/yarqian/psfnet
공식 코드Progressive and Selective Fusion Network for High Dynamic Range Imaging
https://github.com/yashbutala/WASSA
공식 코드Team Phoenix at WASSA 2021: Emotion Analysis on News Stories with Pre-Trained Language Models
https://github.com/yashsharma/mani
공식 코드MaNi: Maximizing Mutual Information for Nuclei Cross-Domain Unsupervised Segmentation
https://github.com/yc-li20/Crossmodal_AEC
공식 코드https://github.com/yc-li20/SER-on-WER-and-Fusion
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.