Change Detection Mamba with Boundary-Specific Supervision

Emphasis on modeling visual context underpins the current high-performance dense prediction models, including those for change detection. However, excessive context modeling tends to cause ambiguous feature representations around object boundaries and possibly overwhelms small or thin objects. This encourages maintaining large feature maps to provide sufficient information for an objective rescue. With the goal of efficiently harvesting global context from large feature maps while getting good sensitivity to change boundaries as well as small or thin changes, we propose a change detection model that features (i) an encoder-decoder architecture with state space model-based feature refinement, and (ii) boundary-specific supervision. Our encoder-decoder is equipped with novel modulated Mamba blocks capable of preserving local correlation and then achieving local-global context mixing. To bridge the gap between local and global information during modulation, we employ a spectral transform on the local features to holistically enhance the information encoded in key frequency components. Moreover, our custom-designed boundary-specific supervision explicitly induces the revision of change boundaries. With these improvements, our Mamba-grounded change detection model can efficiently garner boundary-sensitive large feature maps applicable to various change shapes and scales. Extensive experimental results on four public change detection datasets demonstrate that our method consistently outperforms state-of-the-art competitors in terms of key evaluation metrics.