  We present a simple yet effective prediction module for a one-stage detector.
The main process is conducted in a coarse-to-fine manner. First, the module
roughly adjusts the default boxes to well capture the extent of target objects
in an image. Second, given the adjusted boxes, the module aligns the receptive
field of the convolution filters accordingly, not requiring any embedding
layers. Both steps build a propose-and-attend mechanism, mimicking two-stage
detectors in a highly efficient manner. To verify its effectiveness, we apply
the proposed module to a basic one-stage detector SSD. Our final model achieves
an accuracy comparable to that of state-of-the-art detectors while using a
fraction of their model parameters and computational overheads. Moreover, we
found that the proposed module has two strong applications. 1) The module can
be successfully integrated into a lightweight backbone, further pushing the
efficiency of the one-stage detector. 2) The module also allows
train-from-scratch without relying on any sophisticated base networks as
previous methods do.
