Recent studies have used unsupervised domain adaptive object detection
(UDAOD) methods to bridge the domain gap in remote sensing (RS) images.
However, UDAOD methods typically assume that the source domain data can be
accessed during the domain adaptation process. This setting is often
impractical in the real world due to RS data privacy and transmission
difficulty. To address this challenge, we propose a practical source-free
object detection (SFOD) setting for RS images, which aims to perform target
domain adaptation using only the source pre-trained model. We propose a new
SFOD method for RS images consisting of two parts: perturbed domain generation
and alignment. The proposed multilevel perturbation constructs the perturbed
domain in a simple yet efficient form by perturbing the domain-variant features
at the image level and feature level according to the color and style bias. The
proposed multilevel alignment calculates feature and label consistency between
the perturbed domain and the target domain across the teacher-student network,
and introduces the distillation of feature prototype to mitigate the noise of
pseudo-labels. By requiring the detector to be consistent in the perturbed
domain and the target domain, the detector is forced to focus on
domaininvariant features. Extensive results of three synthetic-to-real
experiments and three cross-sensor experiments have validated the effectiveness
of our method which does not require access to source domain RS images.
Furthermore, experiments on computer vision datasets show that our method can
be extended to other fields as well. Our code will be available at:
this https URL .

该研究提出了一种针对遥感图像的实际无源目标检测方法，通过扰动域生成和多层次对齐实现目标域适应，验证了该方法的有效性，并表明该方法可以扩展到其他领域。

无源领域自适应遥感图像物体检测

Source-free Domain Adaptive Object Detection in Remote Sensing Images

Domain adaptation is crucial in aerial imagery, as the visual representation
of these images can significantly vary based on factors such as geographic
location, time, and weather conditions. Additionally, high-resolution aerial
images often require substantial storage space and may not be readily
accessible to the public. To address these challenges, we propose a novel
Source-Free Object Detection (SFOD) method. Specifically, our approach is built
upon a self-training framework; however, self-training can lead to inaccurate
learning in the absence of labeled training data. To address this issue, we
further integrate Contrastive Language-Image Pre-training (CLIP) to guide the
generation of pseudo-labels, termed CLIP-guided Aggregation. By leveraging
CLIP's zero-shot classification capability, we use it to aggregate scores with
the original predicted bounding boxes, enabling us to obtain refined scores for
the pseudo-labels. To validate the effectiveness of our method, we constructed
two new datasets from different domains based on the DIOR dataset, named DIOR-C
and DIOR-Cloudy. Experiments demonstrate that our method outperforms other
comparative algorithms.

我们提出了一种新颖的无源物体检测（SFOD）方法，利用对比式语言 - 图像预训练（CLIP）引导生成伪标签进行自我训练，以实现在领域适应中的准确学习。实验结果表明，我们的方法优于其他比较算法。

CLIP 引导的无源目标检测在航空图像中

CLIP-guided Source-free Object Detection in Aerial Images

Source-free object detection (SFOD) aims to adapt a source-trained detector
to an unlabeled target domain without access to the labeled source data.
Current SFOD methods utilize a threshold-based pseudo-label approach in the
adaptation phase, which is typically limited to high-confidence pseudo-labels
and results in a loss of information. To address this issue, we propose a new
approach to take full advantage of pseudo-labels by introducing high and low
confidence thresholds. Specifically, the pseudo-labels with confidence scores
above the high threshold are used conventionally, while those between the low
and high thresholds are exploited using the Low-confidence Pseudo-labels
Utilization (LPU) module. The LPU module consists of Proposal Soft Training
(PST) and Local Spatial Contrastive Learning (LSCL). PST generates soft labels
of proposals for soft training, which can mitigate the label mismatch problem.
LSCL exploits the local spatial relationship of proposals to improve the
model's ability to differentiate between spatially adjacent proposals, thereby
optimizing representational features further. Combining the two components
overcomes the challenges faced by traditional methods in utilizing
low-confidence pseudo-labels. Extensive experiments on five cross-domain object
detection benchmarks demonstrate that our proposed method outperforms the
previous SFOD methods, achieving state-of-the-art performance.

我们提出了一种新的方法来充分利用伪标签，通过引入高和低置信度阈值，使用 Proposal Soft Training (PST) 和 Local Spatial Contrastive Learning (LSCL) 模块来利用低置信度的伪标签，进一步优化了代表性特征。实验证明，我们的方法在五个跨领域目标检测基准上表现优于以前的 Source-free object detection (SFOD) 方法，实现了最先进的性能。

利用低置信度伪标签进行无源目标检测

Exploiting Low-confidence Pseudo-labels for Source-free Object Detection

Source-free object detection (SFOD) aims to transfer a detector pre-trained
on a label-rich source domain to an unlabeled target domain without seeing
source data. While most existing SFOD methods generate pseudo labels via a
source-pretrained model to guide training, these pseudo labels usually contain
high noises due to heavy domain discrepancy. In order to obtain better pseudo
supervisions, we divide the target domain into source-similar and
source-dissimilar parts and align them in the feature space by adversarial
learning. Specifically, we design a detection variance-based criterion to
divide the target domain. This criterion is motivated by a finding that larger
detection variances denote higher recall and larger similarity to the source
domain. Then we incorporate an adversarial module into a mean teacher framework
to drive the feature spaces of these two subsets indistinguishable. Extensive
experiments on multiple cross-domain object detection datasets demonstrate that
our proposed method consistently outperforms the compared SFOD methods.

本文提出了一种基于生成伪标签和对抗学习技术的无源目标检测方法，将目标域划分为相似子集和不相似子集，并通过平均教师框架中的对抗模块来提高伪标签的质量，实验表明该方法在跨域目标检测方面表现优异。