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。

面向领域适应的基于区域无关检测器的密集语义增强

Densely Semantic Enhancement for Domain Adaptive Region-free Detectors

Unsupervised Domain Adaptation (UDA) aims at improving the generalization
capability of a model trained on a source domain to perform well on a target
domain for which no labeled data is available. In this paper, we consider the
semantic segmentation of urban scenes and we propose an approach to adapt a
deep neural network trained on synthetic data to real scenes addressing the
domain shift between the two different data distributions. We introduce a novel
UDA framework where a standard supervised loss on labeled synthetic data is
supported by an adversarial module and a self-training strategy aiming at
aligning the two domain distributions. The adversarial module is driven by a
couple of fully convolutional discriminators dealing with different domains:
the first discriminates between ground truth and generated maps, while the
second between segmentation maps coming from synthetic or real world data. The
self-training module exploits the confidence estimated by the discriminators on
unlabeled data to select the regions used to reinforce the learning process.
Furthermore, the confidence is thresholded with an adaptive mechanism based on
the per-class overall confidence. Experimental results prove the effectiveness
of the proposed strategy in adapting a segmentation network trained on
synthetic datasets like GTA5 and SYNTHIA, to real world datasets like
Cityscapes and Mapillary.

本文提出一种基于无监督域自适应（UDA）的方法，针对城市场景的语义分割应用领域，将深度神经网络从合成数据源，适应到真实数据源上，通过引入对抗模块和自训练策略实现充分对齐两个数据分布，模块化的架构使得模型适用范围更广，实验结果表明该方法的有效性和稳健性。