Unsupervised domain adaptive object detection aims to adapt a well-trained
detector from its original source domain with rich labeled data to a new target
domain with unlabeled data. Previous works focus on improving the domain
adaptability of region-based detectors, e.g., Faster-RCNN, through matching
cross-domain instance-level features that are explicitly extracted from a
region proposal network (RPN). However, this is unsuitable for region-free
detectors such as single shot detector (SSD), which perform a dense prediction
from all possible locations in an image and do not have the RPN to encode such
instance-level features. As a result, they fail to align important image
regions and crucial instance-level features between the domains of region-free
detectors. In this work, we propose an adversarial module to strengthen the
cross-domain matching of instance-level features for region-free detectors.
Firstly, to emphasize the important regions of image, the DSEM learns to
predict a transferable foreground enhancement mask that can be utilized to
suppress the background disturbance in an image. Secondly, considering that
region-free detectors recognize objects of different scales using multi-scale
feature maps, the DSEM encodes both multi-level semantic representations and
multi-instance spatial-contextual relationships across different domains.
Finally, the DSEM is pluggable into different region-free detectors, ultimately
achieving the densely semantic feature matching via adversarial learning.
Extensive experiments have been conducted on PASCAL VOC, Clipart, Comic,
Watercolor, and FoggyCityscape benchmarks, and their results well demonstrate
that the proposed approach not only improves the domain adaptability of
region-free detectors but also outperforms existing domain adaptive
region-based detectors under various domain shift settings.

该论文提出一种对域自适应物体检测领域的研究，通过引入对抗模块来加强基于单一检测器的跨域特征匹配，提高了其泛化性能和适应性，对于多种域漂移设置展现了优越的性能。