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/gw-zhong/cider
공식 코드Towards Robust Multimodal Emotion Recognition under Missing Modalities and Distribution Shifts
https://github.com/gxstudy/Graph-Neural-Networks-for-Image-Understanding-Based-on-Multiple-Cues
공식 코드https://github.com/habla-liaa/ser-with-w2v2
공식 코드Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings
https://github.com/haizhongzheng/coverage-centric-coreset-selection
공식 코드https://github.com/hajtos/SparseMIX
공식 코드Efficient mixture model for clustering of sparse high dimensional binary data
https://github.com/hanqi-qi/Position-Bias-Mitigation-in-Emotion-Cause-Analysis
공식 코드Position Bias Mitigation: A Knowledge-Aware Graph Model for Emotion Cause Extraction
https://github.com/hanyu-meng/adapting-leaf
공식 코드https://github.com/haofeixu/flow1d
공식 코드High-Resolution Optical Flow from 1D Attention and Correlation
https://github.com/haoheliu/voicefixer
공식 코드VoiceFixer: A Unified Framework for High-Fidelity Speech Restoration
https://github.com/happycolor/speechformer2
공식 코드SpeechFormer++: A Hierarchical Efficient Framework for Paralinguistic Speech Processing
https://github.com/happycolor/vesper
공식 코드Vesper: A Compact and Effective Pretrained Model for Speech Emotion Recognition
https://github.com/happyjin/nnpm
공식 코드Unsupervised Cross-Lingual Speech Emotion Recognition Using Pseudo Multilabel
https://github.com/hasanhuz/SpanEmo
공식 코드SpanEmo: Casting Multi-label Emotion Classification as Span-prediction
https://github.com/haseebaslam95/MT-PKDOT
공식 코드Multi Teacher Privileged Knowledge Distillation for Multimodal Expression Recognition
https://github.com/haydarevren/aa2d
공식 코드https://github.com/hazyresearch/tabi
공식 코드TABi: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval
https://github.com/hep-lbdl/calibrationpriors
공식 코드Bias and Priors in Machine Learning Calibrations for High Energy Physics
https://github.com/hideitsu/Contact_SymplecticIntegrator
공식 코드Fast symplectic integrator for Nesterov-type acceleration method
https://github.com/hippylib/hippyflow
공식 코드Learning High-Dimensional Parametric Maps via Reduced Basis Adaptive Residual Networks
https://github.com/hku-smart-mobility-lab/ride-sharing-simulator
공식 코드https://github.com/hkzhang95/DynamicRCNN
공식 코드Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training
https://github.com/hltchkust/cantonese-asr
공식 코드Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset
https://github.com/hltchkust/elderly_ser
공식 코드Cross-Lingual Cross-Age Group Adaptation for Low-Resource Elderly Speech Emotion Recognition
https://github.com/hmohebbi/disentangling_representations
공식 코드Disentangling Textual and Acoustic Features of Neural Speech Representations
https://github.com/hongshuochen/DefakeHop
공식 코드DefakeHop: A Light-Weight High-Performance Deepfake Detector
https://github.com/hongwang600/DocRed
공식 코드https://github.com/hoonose/privit
공식 코드https://github.com/hoonose/robust-filter
공식 코드https://github.com/hritikbansal/entigen_emnlp
공식 코드How well can Text-to-Image Generative Models understand Ethical Natural Language Interventions?
https://github.com/hsi-che-lin/improve-ser-via-s2st
공식 코드https://github.com/hszhao/ICNet
공식 코드ICNet for Real-Time Semantic Segmentation on High-Resolution Images
https://github.com/htseq/htseq
공식 코드Analysing high-throughput sequencing data in Python with HTSeq 2.0
https://github.com/huawei-noah/hebo
공식 코드High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning
https://github.com/huawei-noah/noah-research/tree/master/BO/T-LBO
공식 코드High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning
https://github.com/hujingwen6666/MMGCN
공식 코드MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.