  Weakly-supervised object detection (WSOD) models attempt to leverage
image-level annotations in lieu of accurate but costly-to-obtain object
localization labels. This oftentimes leads to substandard object detection and
localization at inference time. To tackle this issue, we propose D2DF2WOD, a
Dual-Domain Fully-to-Weakly Supervised Object Detection framework that
leverages synthetic data, annotated with precise object localization, to
supplement a natural image target domain, where only image-level labels are
available. In its warm-up domain adaptation stage, the model learns a
fully-supervised object detector (FSOD) to improve the precision of the object
proposals in the target domain, and at the same time learns
target-domain-specific and detection-aware proposal features. In its main WSOD
stage, a WSOD model is specifically tuned to the target domain. The feature
extractor and the object proposal generator of the WSOD model are built upon
the fine-tuned FSOD model. We test D2DF2WOD on five dual-domain image
benchmarks. The results show that our method results in consistently improved
object detection and localization compared with state-of-the-art methods.
