  Universal domain adaptive object detection (UniDAOD)is more challenging than
domain adaptive object detection (DAOD) since the label space of the source
domain may not be the same as that of the target and the scale of objects in
the universal scenarios can vary dramatically (i.e, category shift and scale
shift). To this end, we propose US-DAF, namely Universal Scale-Aware Domain
Adaptive Faster RCNN with Multi-Label Learning, to reduce the negative transfer
effect during training while maximizing transferability as well as
discriminability in both domains under a variety of scales. Specifically, our
method is implemented by two modules: 1) We facilitate the feature alignment of
common classes and suppress the interference of private classes by designing a
Filter Mechanism module to overcome the negative transfer caused by category
shift. 2) We fill the blank of scale-aware adaptation in object detection by
introducing a new Multi-Label Scale-Aware Adapter to perform individual
alignment between the corresponding scale for two domains. Experiments show
that US-DAF achieves state-of-the-art results on three scenarios (i.e,
Open-Set, Partial-Set, and Closed-Set) and yields 7.1% and 5.9% relative
improvement on benchmark datasets Clipart1k and Watercolor in particular.
