  One-stage object detectors such as SSD or YOLO already have shown promising
accuracy with small memory footprint and fast speed. However, it is widely
recognized that one-stage detectors have difficulty in detecting small objects
while they are competitive with two-stage methods on large objects. In this
paper, we investigate how to alleviate this problem starting from the SSD
framework. Due to their pyramidal design, the lower layer that is responsible
for small objects lacks strong semantics(e.g contextual information). We
address this problem by introducing a feature combining module that spreads out
the strong semantics in a top-down manner. Our final model StairNet detector
unifies the multi-scale representations and semantic distribution effectively.
Experiments on PASCAL VOC 2007 and PASCAL VOC 2012 datasets demonstrate that
StairNet significantly improves the weakness of SSD and outperforms the other
state-of-the-art one-stage detectors.
