Domain adaptive detection aims to improve the generality of a detector,
learned from the labeled source domain, on the unlabeled target domain. In this
work, drawing inspiration from the concept of stability from the control theory
that a robust system requires to remain consistent both externally and
internally regardless of disturbances, we propose a novel framework that
achieves unsupervised domain adaptive detection through stability analysis. In
specific, we treat discrepancies between images and regions from different
domains as disturbances, and introduce a novel simple but effective Network
Stability Analysis (NSA) framework that considers various disturbances for
domain adaptation. Particularly, we explore three types of perturbations
including heavy and light image-level disturbances and instancelevel
disturbance. For each type, NSA performs external consistency analysis on the
outputs from raw and perturbed images and/or internal consistency analysis on
their features, using teacher-student models. By integrating NSA into Faster
R-CNN, we immediately achieve state-of-the-art results. In particular, we set a
new record of 52.7% mAP on Cityscapes-to-FoggyCityscapes, showing the potential
of NSA for domain adaptive detection. It is worth noticing, our NSA is designed
for general purpose, and thus applicable to one-stage detection model (e.g.,
FCOS) besides the adopted one, as shown by experiments.
this https URL

领域自适应检测通过稳定性分析实现无监督领域自适应检测，探索了不同类型的干扰，并将其整合到 Faster R-CNN 中，取得了最新的研究成果。

基于网络稳定性分析的无监督领域自适应检测

Unsupervised Domain Adaptive Detection with Network Stability Analysis

Domain adaptive detection aims to improve the generalization of detectors on
target domain. To reduce discrepancy in feature distributions between two
domains, recent approaches achieve domain adaption through feature alignment in
different granularities via adversarial learning. However, they neglect the
relationship between multiple granularities and different features in
alignment, degrading detection. Addressing this, we introduce a unified
multi-granularity alignment (MGA)-based detection framework for
domain-invariant feature learning. The key is to encode the dependencies across
different granularities including pixel-, instance-, and category-levels
simultaneously to align two domains. Specifically, based on pixel-level
features, we first develop an omni-scale gated fusion (OSGF) module to
aggregate discriminative representations of instances with scale-aware
convolutions, leading to robust multi-scale detection. Besides, we introduce
multi-granularity discriminators to identify where, either source or target
domains, different granularities of samples come from. Note that, MGA not only
leverages instance discriminability in different categories but also exploits
category consistency between two domains for detection. Furthermore, we present
an adaptive exponential moving average (AEMA) strategy that explores model
assessments for model update to improve pseudo labels and alleviate local
misalignment problem, boosting detection robustness. Extensive experiments on
multiple domain adaption scenarios validate the superiority of MGA over other
approaches on FCOS and Faster R-CNN detectors. Code will be released at
this https URL

该论文提出了一种基于多粒度对齐的检测框架，不仅考虑了不同类别实例之间的区别，还考虑了不同领域中不同粒度样本的区别，并采用自适应指数移动平均策略 for model assessment 来提高检测的鲁棒性。