论文标题
您想看到的更改
The Change You Want to See
论文作者
论文摘要
我们生活在一个充满活力的世界中，事物一直在改变。给定两张同一场景的图像，能够自动检测它们的变化具有各种域中的实际应用。在本文中，我们解决了变更检测问题，即尽管其观点和照明差异有所不同，但仍是在图像对中检测“对象级”变化的目的。为此，我们做出以下四个贡献：（i）我们提出了一种可扩展的方法来通过利用现有对象分割基准来获得大规模更改检测培训数据集； （ii）我们介绍了一个基于共同注意的新颖体系结构，该结构能够隐含地确定图像对之间的对应关系，并以边界框预测的形式找到变化； （iii）我们贡献了四个评估数据集，这些数据集涵盖了各种域和转换，包括合成图像变化，3D场景的实际监视图像以及带有摄像头运动的合成3D场景； （iv）我们在这四个数据集上评估了我们的模型，并证明了零射击并超出训练转换概括。
We live in a dynamic world where things change all the time. Given two images of the same scene, being able to automatically detect the changes in them has practical applications in a variety of domains. In this paper, we tackle the change detection problem with the goal of detecting "object-level" changes in an image pair despite differences in their viewpoint and illumination. To this end, we make the following four contributions: (i) we propose a scalable methodology for obtaining a large-scale change detection training dataset by leveraging existing object segmentation benchmarks; (ii) we introduce a co-attention based novel architecture that is able to implicitly determine correspondences between an image pair and find changes in the form of bounding box predictions; (iii) we contribute four evaluation datasets that cover a variety of domains and transformations, including synthetic image changes, real surveillance images of a 3D scene, and synthetic 3D scenes with camera motion; (iv) we evaluate our model on these four datasets and demonstrate zero-shot and beyond training transformation generalization.
