Feature Manipulation for DDPM based Change Detection
Zhenglin Li Yangchen Huang Mengran Zhu Jingyu Zhang JingHao Chang Houze Liu
Change Detection is a classic task of computer vision that receives a
bi-temporal image pair as input and separates the semantically changed and
unchanged regions of it. The diffusion model is used in image synthesis and as
a feature extractor and has been applied to various downstream tasks. Using
this, a feature map is extracted from the pre-trained diffusion model from the
large-scale data set, and changes are detected through the additional network.
On the one hand, the current diffusion-based change detection approach focuses
only on extracting a good feature map using the diffusion model. It obtains and
uses differences without further adjustment to the created feature map. Our
method focuses on manipulating the feature map extracted from the Diffusion
Model to be more semantically useful, and for this, we propose two methods:
Feature Attention and FDAF. Our model with Feature Attention achieved a
state-of-the-art F1 score (90.18) and IoU (83.86) on the LEVIR-CD dataset.