  Fully supervised change detection methods require difficult to procure
pixel-level labels, while weakly supervised approaches can be trained with
image-level labels. However, most of these approaches require a combination of
changed and unchanged image pairs for training. Thus, these methods can not
directly be used for datasets where only changed image pairs are available. We
present W-CDNet, a novel weakly supervised change detection network that can be
trained with image-level semantic labels. Additionally, W-CDNet can be trained
with two different types of datasets, either containing changed image pairs
only or a mixture of changed and unchanged image pairs. Since we use
image-level semantic labels for training, we simultaneously create a change
mask and label the changed object for single-label images. W-CDNet employs a
W-shaped siamese U-net to extract feature maps from an image pair which then
get compared in order to create a raw change mask. The core part of our model,
the Change Segmentation and Classification (CSC) module, learns an accurate
change mask at a hidden layer by using a custom Remapping Block and then
segmenting the current input image with the change mask. The segmented image is
used to predict the image-level semantic label. The correct label can only be
predicted if the change mask actually marks relevant change. This forces the
model to learn an accurate change mask. We demonstrate the segmentation and
classification performance of our approach and achieve top results on AICD and
HRSCD, two public aerial imaging change detection datasets as well as on a Food
Waste change detection dataset. Our code is available at
https://github.com/PhiAbs/W-CDNet .
