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. Recently, mainstream approaches perform this task
through adversarial learning, yet still suffer from two limitations. First,
they mainly align marginal distribution by unsupervised cross-domain feature
matching, and ignore each feature's categorical and positional information that
can be exploited for conditional alignment; Second, they treat all classes as
equally important for transferring cross-domain knowledge and ignore that
different classes usually have different transferability. In this paper, we
propose a joint adaptive detection framework (JADF) to address the above
challenges. First, an end-to-end joint adversarial adaptation framework for
object detection is proposed, which aligns both marginal and conditional
distributions between domains without introducing any extra hyperparameter.
Next, to consider the transferability of each object class, a metric for
class-wise transferability assessment is proposed, which is incorporated into
the JADF objective for domain adaptation. Further, an extended study from
unsupervised domain adaptation (UDA) to unsupervised few-shot domain adaptation
(UFDA) is conducted, where only a few unlabeled training images are available
in unlabeled target domain. Extensive experiments validate that JADF is
effective in both the UDA and UFDA settings, achieving significant performance
gains over existing state-of-the-art cross-domain detection methods.

本文提出了一个联合自适应检测框架 (JADF)，通过联合、条件式对齐特征空间和返回类别空间，以提高无监督域自适应目标检测的性能，并提出了类别可迁移度评估指标来考虑每个对象类别的可迁移度，实现了无监督域自适应目标检测中 - state-of-the-art 性能。