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Existing visual change detectors usually adopt CNNs or Transformers forfeature representation learning and focus on learning effective representationfor the changed regions between images. Although good performance can beobtained by enhancing the features of the change regions, however, these worksare still limited mainly due to the ignorance of mining the unchangedbackground context information. It is known that one main challenge for changedetection is how to obtain the consistent representations for two imagesinvolving different variations, such as spatial variation, sunlight intensity,etc. In this work, we demonstrate that carefully mining the common backgroundinformation provides an important cue to learn the consistent representationsfor the two images which thus obviously facilitates the visual change detectionproblem. Based on this observation, we propose a novel Visual changeTransformer (VcT) model for visual change detection problem. To be specific, ashared backbone network is first used to extract the feature maps for the givenimage pair. Then, each pixel of feature map is regarded as a graph node and thegraph neural network is proposed to model the structured information for coarsechange map prediction. Top-K reliable tokens can be mined from the map andrefined by using the clustering algorithm. Then, these reliable tokens areenhanced by first utilizing self/cross-attention schemes and then interactingwith 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 theeffectiveness of our proposed VcT model.

VcT: Visual change Transformer for Remote Sensing Image Change Detection