  Despite excellent progress has been made, the performance of deep learning
based algorithms still heavily rely on specific datasets, which are difficult
to extend due to labor-intensive labeling. Moreover, because of the advancement
of new applications, initial definition of data annotations might not always
meet the requirements of new functionalities. Thus, there is always a great
demand in customized data annotations. To address the above issues, we propose
the Few-Shot Model Adaptation (FSMA) framework and demonstrate its potential on
several important tasks on Faces. The FSMA first acquires robust facial image
embeddings by training an adversarial auto-encoder using large-scale unlabeled
data. Then the model is equipped with feature adaptation and fusion layers, and
adapts to the target task efficiently using a minimal amount of annotated
images. The FSMA framework is prominent in its versatility across a wide range
of facial image applications. The FSMA achieves state-of-the-art few-shot
landmark detection performance and it offers satisfying solutions for few-shot
face segmentation, stylization and facial shadow removal tasks for the first
time.
