https://github.com/amaai-lab/cross-dataset-emotion-alignment
공식 코드Leveraging LLM Embeddings for Cross Dataset Label Alignment and Zero Shot Music Emotion Prediction
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/amaai-lab/muvi
공식 코드https://github.com/amanshenoy/multilogue-net
공식 코드https://github.com/amazon-research/wikiwiki-dataset
공식 코드Instilling Type Knowledge in Language Models via Multi-Task QA
https://github.com/americast/electoral
공식 코드https://github.com/amr-gomaa/ML-PersRef
공식 코드https://github.com/amu-cai/nemo
공식 코드https://github.com/amy-tabb/CASS
공식 코드Using cameras for precise measurement of two-dimensional plant features: CASS
https://github.com/andralex8/polycam
공식 코드Poly-CAM: High resolution class activation map for convolutional neural networks
https://github.com/andreimargeloiu/gcondnet
공식 코드GCondNet: A Novel Method for Improving Neural Networks on Small High-Dimensional Tabular Data
https://github.com/andrewhou1/Shadow-Mask-Face-Relighting
공식 코드Towards High Fidelity Face Relighting with Realistic Shadows
https://github.com/anfelopera/lineqGPR
공식 코드High-dimensional additive Gaussian processes under monotonicity constraints
https://github.com/angusmaiden/complex-fer
공식 코드Complex Facial Expression Recognition Using Deep Knowledge Distillation of Basic Features
https://github.com/animadversio/actmax-optimizer-dev
공식 코드High-performance Evolutionary Algorithms for Online Neuron Control
https://github.com/anirbanc96/mmmd-boost-kernel-two-sample
공식 코드https://github.com/anishacharya/Optimization-Mavericks
공식 코드Robust Training in High Dimensions via Block Coordinate Geometric Median Descent
https://github.com/anita-hu/MSAF
공식 코드https://github.com/ankit-vaidya19/eacl_vardial2023
공식 코드https://github.com/annehartebrodt/fc-federated-svd
공식 코드Federated singular value decomposition for high dimensional data
https://github.com/annehartebrodt/federated-svd
공식 코드Federated singular value decomposition for high dimensional data
https://github.com/anoncsc/csc
공식 코드https://github.com/anshulwadhawan/emotion_detection
공식 코드Towards Emotion Recognition in Hindi-English Code-Mixed Data: A Transformer Based Approach
https://github.com/anton-kashkin/hifi_vc
공식 코드https://github.com/antoninschrab/fl-mmdagg
공식 코드https://github.com/antoninschrab/mmdagg
공식 코드https://github.com/antran89/two-stream-fcan
공식 코드Two-stream Flow-guided Convolutional Attention Networks for Action Recognition
https://github.com/anugunjnaman/fixed-maml
공식 코드Fixed-MAML for Few Shot Classification in Multilingual Speech Emotion Recognition
https://github.com/apoorva-nunna/mustard_plus_plus
공식 코드https://github.com/appen/uhv-ots-speech
공식 코드Scalable Data Annotation Pipeline for High-Quality Large Speech Datasets Development
https://github.com/apple/ml-cvnets
공식 코드https://github.com/aquemy/DPSO_experiments
공식 코드https://github.com/arassadin/emotiw2017
공식 코드Group-level Emotion Recognition using Transfer Learning from Face Identification
https://github.com/arazd/miread
공식 코드MIReAD: Simple Method for Learning High-quality Representations from Scientific Documents
https://github.com/areffarhadi/masked_face_emotion_recognition
공식 코드Facial Emotion Recognition Under Mask Coverage Using a Data Augmentation Technique
https://github.com/arnav10goel/camulenet
공식 코드https://github.com/ars22/meta-learning-eval-id-vs-ood
공식 코드Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.