  We propose a Dynamic Scale Training paradigm (abbreviated as DST) to mitigate
scale variation challenge in object detection. Previous strategies like image
pyramid, multi-scale training, and their variants are aiming at preparing
scale-invariant data for model optimization. However, the preparation procedure
is unaware of the following optimization process that restricts their
capability in handling the scale variation. Instead, in our paradigm, we use
feedback information from the optimization process to dynamically guide the
data preparation. The proposed method is surprisingly simple yet obtains
significant gains (2%+ Average Precision on MS COCO dataset), outperforming
previous methods. Experimental results demonstrate the efficacy of our proposed
DST method towards scale variation handling. It could also generalize to
various backbones, benchmarks, and other challenging downstream tasks like
instance segmentation. It does not introduce inference overhead and could serve
as a free lunch for general detection configurations. Besides, it also
facilitates efficient training due to fast convergence. Code and models are
available at github.com/yukang2017/Stitcher.
