A Parameter-Efficient Differentiable Active Contour Network for Precisely Building Instance Segmentation

Methods combining deep neural networks (DNN) and active contour models (ACM) have shown effective performance in building segmentation tasks. However, due to the independent structural design, it suffers from a large amount of parameters and computational cost. To address the above problem and further explore the performance effect of the coupling DNN and ACM methods, we present a Parameter-efficient Differentiable Active Contour (PDAC) network for precisely segmenting buildings in remote sensing scenarios. Specifically, an adapter is applied to fine-tune the encoder, instead of initializing a new one. Based on it, a semantic segmentation model can achieve further localization improvement by introducing a minor amount of parameters. Experiments on two BE datasets show that PDAC achieves better performance with nearly half of the computational resources than the baseline.