We present a novel, training-free approach to scene change detection. Our
method leverages tracking models, which inherently perform change detection
between consecutive frames of video by identifying common objects and detecting
new or missing objects. Specifically, our method takes advantage of the change
detection effect of the tracking model by inputting reference and query images
instead of consecutive frames. Furthermore, we focus on the content gap and
style gap between two input images in change detection, and address both issues
by proposing adaptive content threshold and style bridging layers,
respectively. Finally, we extend our approach to video to exploit rich temporal
information, enhancing scene change detection performance. We compare our
approach and baseline through various experiments. While existing train-based
baseline tend to specialize only in the trained domain, our method shows
consistent performance across various domains, proving the competitiveness of
our approach.

我们提出了一种新颖的无需训练的场景变化检测方法，该方法利用跟踪模型进行连续帧变化检测，通过识别共同物体和检测新的或缺失的物体。我们还通过引入自适应内容阈值和样式桥接层解决了输入图像之间的内容差异和样式差异问题，并将该方法扩展到视频以提高场景变化检测性能。通过各种实验证明，我们的方法在不同领域上表现出一致的性能，证明了我们方法的竞争力。

零样本场景变化检测

Zero-Shot Scene Change Detection

Despite the success of deep learning-based object detection methods in recent
years, it is still challenging to make the object detector reliable in adverse
weather conditions such as rain and snow. For the robust performance of object
detectors, unsupervised domain adaptation has been utilized to adapt the
detection network trained on clear weather images to adverse weather images.
While previous methods do not explicitly address weather corruption during
adaptation, the domain gap between clear and adverse weather can be decomposed
into two factors with distinct characteristics: a style gap and a weather gap.
In this paper, we present an unsupervised domain adaptation framework for
object detection that can more effectively adapt to real-world environments
with adverse weather conditions by addressing these two gaps separately. Our
method resolves the style gap by concentrating on style-related information of
high-level features using an attention module. Using self-supervised
contrastive learning, our framework then reduces the weather gap and acquires
instance features that are robust to weather corruption. Extensive experiments
demonstrate that our method outperforms other methods for object detection in
adverse weather conditions.

通过解决样式差异和天气差异两个方面的问题，本文提出了一种针对目标检测的无监督领域适应框架，能够更有效地适应恶劣天气条件下的真实环境，并在恶劣天气条件下的目标检测方面优于其他方法。