  Detecting arbitrarily oriented tiny objects poses intense challenges to
existing detectors, especially for label assignment. Despite the exploration of
adaptive label assignment in recent oriented object detectors, the extreme
geometry shape and limited feature of oriented tiny objects still induce severe
mismatch and imbalance issues. Specifically, the position prior, positive
sample feature, and instance are mismatched, and the learning of extreme-shaped
objects is biased and unbalanced due to little proper feature supervision. To
tackle these issues, we propose a dynamic prior along with the coarse-to-fine
assigner, dubbed DCFL. For one thing, we model the prior, label assignment, and
object representation all in a dynamic manner to alleviate the mismatch issue.
For another, we leverage the coarse prior matching and finer posterior
constraint to dynamically assign labels, providing appropriate and relatively
balanced supervision for diverse instances. Extensive experiments on six
datasets show substantial improvements to the baseline. Notably, we obtain the
state-of-the-art performance for one-stage detectors on the DOTA-v1.5,
DOTA-v2.0, and DIOR-R datasets under single-scale training and testing. Codes
are available at https://github.com/Chasel-Tsui/mmrotate-dcfl.
