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/yc-li20/semi-supervised-training
공식 코드Semi-Supervised Cognitive State Classification from Speech with Multi-View Pseudo-Labeling
https://github.com/ydk122024/ccim
공식 코드https://github.com/ydongd/ercmc
공식 코드Exploiting Pseudo Future Contexts for Emotion Recognition in Conversations
https://github.com/yeeeqichen/fits
공식 코드FiTs: Fine-grained Two-stage Training for Knowledge-aware Question Answering
https://github.com/yehudit96/coreferrability
공식 코드https://github.com/yehudit96/event_entity_coref_ecb_plus
공식 코드https://github.com/yelboudouri/EmoNeXt
공식 코드https://github.com/yeonsw/squid
공식 코드https://github.com/yi-ding-cs/LGG
공식 코드LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer Interface
https://github.com/yi-ding-cs/TSception
공식 코드TSception: Capturing Temporal Dynamics and Spatial Asymmetry from EEG for Emotion Recognition
https://github.com/yi-ding-cs/emt
공식 코드EmT: A Novel Transformer for Generalized Cross-subject EEG Emotion Recognition
https://github.com/yi-ding-cs/masa-tcn
공식 코드https://github.com/yi94code/HRSOD
공식 코드https://github.com/yichen14/fastadasp
공식 코드FastAdaSP: Multitask-Adapted Efficient Inference for Large Speech Language Model
https://github.com/yilingchung/multilingualcn-classification
공식 코드https://github.com/yingjie7/BiosERC
공식 코드BiosERC: Integrating Biography Speakers Supported by LLMs for ERC Tasks
https://github.com/yingjie7/mlingual_multilabel_emo_detection
공식 코드JNLP at SemEval-2025 Task 11: Cross-Lingual Multi-Label Emotion Detection Using Generative Models
https://github.com/yingqinghe/lvdm
공식 코드Latent Video Diffusion Models for High-Fidelity Long Video Generation
https://github.com/yinwenpeng/FEVER
공식 코드TwoWingOS: A Two-Wing Optimization Strategy for Evidential Claim Verification
https://github.com/yiqun-wang/hfs
공식 코드HF-NeuS: Improved Surface Reconstruction Using High-Frequency Details
https://github.com/yizhiwang96/deepvecfont
공식 코드DeepVecFont: Synthesizing High-quality Vector Fonts via Dual-modality Learning
https://github.com/yjxiong/caffe
공식 코드https://github.com/yk-pku/two-shot-video-object-segmentation
공식 코드https://github.com/ylincen/palm
공식 코드Unsupervised Discretization by Two-dimensional MDL-based Histogram
https://github.com/yoda-lab/simple-incentives-for-ridesharing
공식 코드Using Simple Incentives to Improve Two-Sided Fairness in Ridesharing Systems
https://github.com/yongaifadian1/Dynamic-Parameter-Memory_DPM
공식 코드https://github.com/yongcaoplus/probingchatgpt
공식 코드Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study
https://github.com/yqx7150/HFDAEP
공식 코드Learning Priors in High-frequency Domain for Inverse Imaging Reconstruction
https://github.com/yqx7150/hgm
공식 코드High-dimensional Assisted Generative Model for Color Image Restoration
https://github.com/yrf1/normsage
공식 코드NormSAGE: Multi-Lingual Multi-Cultural Norm Discovery from Conversations On-the-Fly
https://github.com/ysaikai/rlir
공식 코드Deep reinforcement learning for irrigation scheduling using high-dimensional sensor feedback
https://github.com/yshin12/fadtwo
공식 코드https://github.com/yu-fangxu/eacl
공식 코드Emotion-Anchored Contrastive Learning Framework for Emotion Recognition in Conversation
https://github.com/yuchen-zhu/kernel_proxies
공식 코드Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction
https://github.com/yunboouyang/EBclassifier
공식 코드An Empirical Bayes Approach for High Dimensional Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.