https://github.com/WYT8506/MultimodalCertification
공식 코드MMCert: Provable Defense against Adversarial Attacks to Multi-modal Models
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/WeiHao97/DIVA
공식 코드A Tale of Two Models: Constructing Evasive Attacks on Edge Models
https://github.com/Wisteria30/GIM-RL
공식 코드https://github.com/XingangPan/IBN-Net
공식 코드Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/XuanhaoLiu/mixEEG
공식 코드mixEEG: Enhancing EEG Federated Learning for Cross-subject EEG Classification with Tailored mixup
https://github.com/XuzheZ/PTNet
공식 코드PTNet: A High-Resolution Infant MRI Synthesizer Based on Transformer
https://github.com/YStrauch/evolving-cnn-topologies-for-image-recognition
공식 코드Two Novel Performance Improvements for Evolving CNN Topologies
https://github.com/YalDan/hf.econometrics
공식 코드https://github.com/YanhaoHuang23/FAT
공식 코드A novel Fourier Adjacency Transformer for advanced EEG emotion recognition
https://github.com/YingjieHu/POI_Name
공식 코드An empirical study on the names of points of interest and their changes with geographic distance
https://github.com/YoYo000/MVSNet
공식 코드Recurrent MVSNet for High-resolution Multi-view Stereo Depth Inference
https://github.com/YuanGongND/ast
공식 코드https://github.com/YuanGongND/ssast
공식 코드https://github.com/YuanLi95/Semveal2020-Task8-emotion-analysis
공식 코드YNU-HPCC at SemEval-2020 Task 8: Using a Parallel-Channel Model for Memotion Analysis
https://github.com/YueLiao/CDN
공식 코드Mining the Benefits of Two-stage and One-stage HOI Detection
https://github.com/YunlongJiao/kernrank
공식 코드The Weighted Kendall and High-order Kernels for Permutations
https://github.com/YunlongJiao/weightedkendall
공식 코드The Weighted Kendall and High-order Kernels for Permutations
https://github.com/Zebreu/ConvolutionalEmotion
공식 코드Real-time emotion recognition for gaming using deep convolutional network features
https://github.com/Zedd1558/TwoStreamSepConvLSTM_ViolenceDetection
공식 코드Efficient Two-Stream Network for Violence Detection Using Separable Convolutional LSTM
https://github.com/ZeyuGaoAi/Instance_based_Vision_Transformer
공식 코드https://github.com/ZhangLeUestc/PersEmoN
공식 코드PersEmoN: A Deep Network for Joint Analysis of Apparent Personality, Emotion and Their Relationship
https://github.com/ZhangShiyue/ChrEn
공식 코드ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization
https://github.com/ZhaoJ9014/face.evoLVe.PyTorch
공식 코드https://github.com/Zhenyu-LIAO/RMT4RFM
공식 코드On the Spectrum of Random Features Maps of High Dimensional Data
https://github.com/ZilongTan/arLMM
공식 코드Scalable Algorithms for Learning High-Dimensional Linear Mixed Models
https://github.com/ZizhouJia/PDANet
공식 코드PDANet: Polarity-consistent Deep Attention Network for Fine-grained Visual Emotion Regression
https://github.com/a-koufakou/augemotiondetection
공식 코드Data Augmentation for Emotion Detection in Small Imbalanced Text Data
https://github.com/a-moin/flexemg
공식 코드https://github.com/a-mos/High_Resolution_Image_Inpainting
공식 코드https://github.com/aalto-speech/mtkd4ser
공식 코드Multi-Teacher Language-Aware Knowledge Distillation for Multilingual Speech Emotion Recognition
https://github.com/aboriginer/hfs-sde
공식 코드https://github.com/adobe-research/auto-wire-removal
공식 코드https://github.com/adonis-galaxy/dspoint
공식 코드DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion
https://github.com/adrianastan/tbdm-net
공식 코드TBDM-Net: Bidirectional Dense Networks with Gender Information for Speech Emotion Recognition
https://github.com/adrianhutter/learning_with_opponent_transparency
공식 코드https://github.com/afrl-ri/adversarialDistance
공식 코드Harnessing Adversarial Distances to Discover High-Confidence Errors
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.