arXiv:2207.10201
Hybrid CNN-Transformer Model For Facial Affect Recognition In the ABAW4 Challenge
원문 초록
This paper describes our submission to the fourth Affective Behavior Analysis (ABAW) competition. We proposed a hybrid CNN-Transformer model for the Multi-Task-Learning (MTL) and Learning from Synthetic Data (LSD) task. Experimental results on validation dataset shows that our method achieves better performance than baseline model, which verifies that the effectiveness of proposed network.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Robust-Multi-Task Gradient Boosting
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation
SonicVerse: Multi-Task Learning for Music Feature-Informed Captioning
CALM: Consensus-Aware Localized Merging for Multi-Task Learning
Training-free LLM Merging for Multi-task Learning
Non-Contact Health Monitoring During Daily Personal Care Routines
토론
이 논문과 연결된 포럼 글이 아직 없습니다.