  In recent years, significant progress has been made in the research of facial
landmark detection. However, few prior works have thoroughly discussed about
models for practical applications. Instead, they often focus on improving a
couple of issues at a time while ignoring the others. To bridge this gap, we
aim to explore a practical model that is accurate, robust, efficient,
generalizable, and end-to-end trainable at the same time. To this end, we first
propose a baseline model equipped with one transformer decoder as detection
head. In order to achieve a better accuracy, we further propose two lightweight
modules, namely dynamic query initialization (DQInit) and query-aware memory
(QAMem). Specifically, DQInit dynamically initializes the queries of decoder
from the inputs, enabling the model to achieve as good accuracy as the ones
with multiple decoder layers. QAMem is designed to enhance the discriminative
ability of queries on low-resolution feature maps by assigning separate memory
values to each query rather than a shared one. With the help of QAMem, our
model removes the dependence on high-resolution feature maps and is still able
to obtain superior accuracy. Extensive experiments and analysis on three
popular benchmarks show the effectiveness and practical advantages of the
proposed model. Notably, our model achieves new state of the art on WFLW as
well as competitive results on 300W and COFW, while still running at 50+ FPS.
