In this paper, we focus on a realistic yet challenging task, Single Domain
Generalization Object Detection (S-DGOD), where only one source domain's data
can be used for training object detectors, but have to generalize multiple
distinct target domains. In S-DGOD, both high-capacity fitting and
generalization abilities are needed due to the task's complexity.
Differentiable Neural Architecture Search (NAS) is known for its high capacity
for complex data fitting and we propose to leverage Differentiable NAS to solve
S-DGOD. However, it may confront severe over-fitting issues due to the feature
imbalance phenomenon, where parameters optimized by gradient descent are biased
to learn from the easy-to-learn features, which are usually non-causal and
spuriously correlated to ground truth labels, such as the features of
background in object detection data. Consequently, this leads to serious
performance degradation, especially in generalizing to unseen target domains
with huge domain gaps between the source domain and target domains. To address
this issue, we propose the Generalizable loss (G-loss), which is an OoD-aware
objective, preventing NAS from over-fitting by using gradient descent to
optimize parameters not only on a subset of easy-to-learn features but also the
remaining predictive features for generalization, and the overall framework is
named G-NAS. Experimental results on the S-DGOD urban-scene datasets
demonstrate that the proposed G-NAS achieves SOTA performance compared to
baseline methods. Codes are available at this https URL

我们提出了一种名为 G-NAS 的整体框架，利用可微分的神经结构搜索 (Differentiable NAS) 来解决单域泛化目标检测 (S-DGOD) 问题，通过引入 Generalizable loss (G-loss) 来防止过拟合，并在 S-DGOD 城市场景数据集上取得了 SOTA 表现。