  Rotated bounding boxes drastically reduce output ambiguity of elongated
objects, making it superior to axis-aligned bounding boxes. Despite the
effectiveness, rotated detectors are not widely employed. Annotating rotated
bounding boxes is such a laborious process that they are not provided in many
detection datasets where axis-aligned annotations are used instead. In this
paper, we propose a framework that allows the model to predict precise rotated
boxes only requiring cheaper axis-aligned annotation of the target dataset 1.
To achieve this, we leverage the fact that neural networks are capable of
learning richer representation of the target domain than what is utilized by
the task. The under-utilized representation can be exploited to address a more
detailed task. Our framework combines task knowledge of an out-of-domain source
dataset with stronger annotation and domain knowledge of the target dataset
with weaker annotation. A novel assignment process and projection loss are used
to enable the co-training on the source and target datasets. As a result, the
model is able to solve the more detailed task in the target domain, without
additional computation overhead during inference. We extensively evaluate the
method on various target datasets including fresh-produce dataset, HRSC2016 and
SSDD. Results show that the proposed method consistently performs on par with
the fully supervised approach.
