  Existing visual change detectors usually adopt CNNs or Transformers for
feature representation learning and focus on learning effective representation
for the changed regions between images. Although good performance can be
obtained by enhancing the features of the change regions, however, these works
are still limited mainly due to the ignorance of mining the unchanged
background context information. It is known that one main challenge for change
detection is how to obtain the consistent representations for two images
involving different variations, such as spatial variation, sunlight intensity,
etc. In this work, we demonstrate that carefully mining the common background
information provides an important cue to learn the consistent representations
for the two images which thus obviously facilitates the visual change detection
problem. Based on this observation, we propose a novel Visual change
Transformer (VcT) model for visual change detection problem. To be specific, a
shared backbone network is first used to extract the feature maps for the given
image pair. Then, each pixel of feature map is regarded as a graph node and the
graph neural network is proposed to model the structured information for coarse
change map prediction. Top-K reliable tokens can be mined from the map and
refined by using the clustering algorithm. Then, these reliable tokens are
enhanced by first utilizing self/cross-attention schemes and then interacting
with original features via an anchor-primary attention learning module.
Finally, the prediction head is proposed to get a more accurate change map.
Extensive experiments on multiple benchmark datasets validated the
effectiveness of our proposed VcT model.
