  The past few years have witnessed the immense success of object detection,
while current excellent detectors struggle on tackling size-limited instances.
Concretely, the well-known challenge of low overlaps between the priors and
object regions leads to a constrained sample pool for optimization, and the
paucity of discriminative information further aggravates the recognition. To
alleviate the aforementioned issues, we propose CFINet, a two-stage framework
tailored for small object detection based on the Coarse-to-fine pipeline and
Feature Imitation learning. Firstly, we introduce Coarse-to-fine RPN (CRPN) to
ensure sufficient and high-quality proposals for small objects through the
dynamic anchor selection strategy and cascade regression. Then, we equip the
conventional detection head with a Feature Imitation (FI) branch to facilitate
the region representations of size-limited instances that perplex the model in
an imitation manner. Moreover, an auxiliary imitation loss following supervised
contrastive learning paradigm is devised to optimize this branch. When
integrated with Faster RCNN, CFINet achieves state-of-the-art performance on
the large-scale small object detection benchmarks, SODA-D and SODA-A,
underscoring its superiority over baseline detector and other mainstream
detection approaches.
