Change detection (CD) aims to detect change regions within an image pair
captured at different times, playing a significant role for diverse real-world
applications. Nevertheless, most of existing works focus on designing advanced
network architectures to map the feature difference to the final change map
while ignoring the influence of the quality of the feature difference. In this
paper, we study the CD from a new perspective, i.e., how to optimize the
feature difference to highlight changes and suppress unchanged regions, and
propose a novel module denoted as iterative difference-enhanced transformers
(IDET). IDET contains three transformers: two transformers for extracting the
long-range information of the two images and one transformer for enhancing the
feature difference. In contrast to the previous transformers, the third
transformer takes the outputs of the first two transformers to guide the
enhancement of the feature difference iteratively. To achieve more effective
refinement, we further propose the multi-scale IDET-based change detection that
uses multi-scale representations of the images for multiple feature difference
refinements and proposes a coarse-to-fine fusion strategy to combine all
refinements. Our final CD method outperforms seven state-of-the-art methods on
six large-scale datasets under diverse application scenarios, which
demonstrates the importance of feature difference enhancements and the
effectiveness of IDET.Comment: conferenc