Unsupervised domain adaptation (UDA) aims to transfer a model learned using
labeled data from the source domain to unlabeled data in the target domain. To
address the large domain gap issue between the source and target domains, we
propose a novel regularization method for domain adaptive object detection,
BlenDA, by generating the pseudo samples of the intermediate domains and their
corresponding soft domain labels for adaptation training. The intermediate
samples are generated by dynamically blending the source images with their
corresponding translated images using an off-the-shelf pre-trained
text-to-image diffusion model which takes the text label of the target domain
as input and has demonstrated superior image-to-image translation quality.
Based on experimental results from two adaptation benchmarks, our proposed
approach can significantly enhance the performance of the state-of-the-art
domain adaptive object detector, Adversarial Query Transformer (AQT).
Particularly, in the Cityscapes to Foggy Cityscapes adaptation, we achieve an
impressive 53.4% mAP on the Foggy Cityscapes dataset, surpassing the previous
state-of-the-art by 1.5%. It is worth noting that our proposed method is also
applicable to various paradigms of domain adaptive object detection. The code
is available at:this https URL

通过生成中间领域的伪样本及其对应的软域标签以进行自适应训练，我们提出了一种用于领域自适应目标检测的新型正则化方法 BlenDA，旨在解决源域与目标域之间存在的较大领域差异问题。根据两个适应性基准的实验结果，我们提出的方法能显著提高最先进的领域自适应目标检测器 Adversarial Query Transformer (AQT) 的性能，在 Cityscapes 至 Foggy Cityscapes 自适应中，我们在 Foggy Cityscapes 数据集上实现了 53.4% 的平均准确率 (mAP)，超过了先前的最先进方法 1.5%。值得注意的是，我们提出的方法也适用于各种领域自适应目标检测范式。

BlenDA：通过基于扩散的混合实现域自适应目标检测

BlenDA: Domain Adaptive Object Detection through diffusion-based  blending

Knowledge distillation (KD) is a successful approach for deep neural network
acceleration, with which a compact network (student) is trained by mimicking
the softmax output of a pre-trained high-capacity network (teacher). In
tradition, KD usually relies on access to the training samples and the
parameters of the white-box teacher to acquire the transferred knowledge.
However, these prerequisites are not always realistic due to storage costs or
privacy issues in real-world applications. Here we propose the concept of
decision-based black-box (DB3) knowledge distillation, with which the student
is trained by distilling the knowledge from a black-box teacher (parameters are
not accessible) that only returns classes rather than softmax outputs. We start
with the scenario when the training set is accessible. We represent a sample's
robustness against other classes by computing its distances to the teacher's
decision boundaries and use it to construct the soft label for each training
sample. After that, the student can be trained via standard KD. We then extend
this approach to a more challenging scenario in which even accessing the
training data is not feasible. We propose to generate pseudo samples
distinguished by the teacher's decision boundaries to the largest extent and
construct soft labels for them, which are used as the transfer set. We evaluate
our approaches on various benchmark networks and datasets and experiment
results demonstrate their effectiveness. Codes are available at:
this https URL

该研究提出一种基于决策边界的黑盒知识蒸馏方法（DB3），包括由教师决策边界构造软标签，以及生成伪样本以代表未知数据，皆不需要接触教师参数，结果表明该方法在各种基准网络和数据集上都表现出有效性。