  Fully supervised change detection methods have achieved significant
advancements in performance, yet they depend severely on acquiring costly
pixel-level labels. Considering that the patch-level annotations also contain
abundant information corresponding to both changed and unchanged objects in
bi-temporal images, an intuitive solution is to segment the changes with
patch-level annotations. How to capture the semantic variations associated with
the changed and unchanged regions from the patch-level annotations to obtain
promising change results is the critical challenge for the weakly supervised
change detection task. In this paper, we propose a memory-supported transformer
(MS-Former), a novel framework consisting of a bi-directional attention block
(BAB) and a patch-level supervision scheme (PSS) tailored for weakly supervised
change detection with patch-level annotations. More specifically, the BAM
captures contexts associated with the changed and unchanged regions from the
temporal difference features to construct informative prototypes stored in the
memory bank. On the other hand, the BAM extracts useful information from the
prototypes as supplementary contexts to enhance the temporal difference
features, thereby better distinguishing changed and unchanged regions. After
that, the PSS guides the network learning valuable knowledge from the
patch-level annotations, thus further elevating the performance. Experimental
results on three benchmark datasets demonstrate the effectiveness of our
proposed method in the change detection task. The demo code for our work will
be publicly available at \url{https://github.com/guanyuezhen/MS-Former}.
