Personalized Adaptation

Personalized adaptation aims to tailor machine learning models to individual users, overcoming limitations of generic models that struggle with inter-personal variability. Current research focuses on efficient adaptation techniques, often employing meta-learning or parameter-efficient fine-tuning methods within architectures like transformers and CNN-LSTMs, to minimize the need for extensive user-specific data. This work is significant because it improves the accuracy and robustness of applications ranging from gaze estimation and gesture recognition to emotion recognition and 3D human motion generation, leading to more user-friendly and effective systems.