Weakly-supervised change detection (WSCD) aims to detect pixel-level changes
with only image-level annotations. Owing to its label efficiency, WSCD is
drawing increasing attention recently. However, current WSCD methods often
encounter the challenge of change missing and fabricating, i.e., the
inconsistency between image-level annotations and pixel-level predictions.
Specifically, change missing refer to the situation that the WSCD model fails
to predict any changed pixels, even though the image-level label indicates
changed, and vice versa for change fabricating. To address this challenge, in
this work, we leverage global-scale and local-scale priors in WSCD and propose
two components: a Dilated Prior (DP) decoder and a Label Gated (LG) constraint.
The DP decoder decodes samples with the changed image-level label, skips
samples with the unchanged label, and replaces them with an all-unchanged
pixel-level label. The LG constraint is derived from the correspondence between
changed representations and image-level labels, penalizing the model when it
mispredicts the change status. Additionally, we develop TransWCD, a simple yet
powerful transformer-based model, showcasing the potential of weakly-supervised
learning in change detection. By integrating the DP decoder and LG constraint
into TransWCD, we form TransWCD-DL. Our proposed TransWCD and TransWCD-DL
achieve significant +6.33% and +9.55% F1 score improvements over the
state-of-the-art methods on the WHU-CD dataset, respectively. Some performance
metrics even exceed several fully-supervised change detection (FSCD)
competitors. Code will be available at
this https URL

通过引入全局尺度和局部尺度先验以及新的组件，本文提出了一种基于弱监督学习的变化检测模型 TransWCD，通过将 DP 解码器和 LG 约束与 TransWCD 相结合，构建了 TransWCD-DL，实验结果表明，与当前最先进的方法相比，TransWCD 和 TransWCD-DL 在 WHU-CD 数据集上可以获得显著的 F1 得分提升。