  Object detection is an essential step towards holistic scene understanding.
Most existing object detection algorithms attend to certain object areas once
and then predict the object locations. However, neuroscientists have revealed
that humans do not look at the scene in fixed steadiness. Instead, human eyes
move around, locating informative parts to understand the object location. This
active perceiving movement process is called \textit{saccade}.
  %In this paper, Inspired by such mechanism, we propose a fast and accurate
object detector called \textit{SaccadeNet}. It contains four main modules, the
\cenam, the \coram, the \atm, and the \aggatt, which allows it to attend to
different informative object keypoints, and predict object locations from
coarse to fine. The \coram~is used only during training to extract more
informative corner features which brings free-lunch performance boost. On the
MS COCO dataset, we achieve the performance of 40.4\% mAP at 28 FPS and 30.5\%
mAP at 118 FPS. Among all the real-time object detectors, %that can run faster
than 25 FPS, our SaccadeNet achieves the best detection performance, which
demonstrates the effectiveness of the proposed detection mechanism.
