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.

我们提出了 US-DAF 方法来解决通用域自适应目标检测中的负迁移问题，提高在各种尺度下的迁移性和区分性。该方法通过两个模块实现：1）设计一个过滤机制模块来促进公共类的特征对齐并抑制私有类的干扰来克服由于类别偏移引起的负迁移。2）引入新的多标签尺度适应器来进行两个域之间相应尺度的个体对齐。实验表明，US-DAF 方法在三种场景下均达到了最先进的结果，并在 Clipart1k 和 Watercolor 基准数据集上获得了 7.1％和 5.9％的相对改进。