SRSNetwork: Siamese Reconstruction-Segmentation Networks based on
  Dynamic-Parameter Convolution
Bingkun Nian Fenghe Tang Jianrui Ding Pingping Zhang Jie Yang S. Kevin Zhou Wei Liu
In this paper, we present a high-performance deep neural network for weak
target image segmentation, including medical image segmentation and infrared
image segmentation. To this end, this work analyzes the existing dynamic
convolutions and proposes dynamic parameter convolution (DPConv). Furthermore,
it reevaluates the relationship between reconstruction tasks and segmentation
tasks from the perspective of DPConv, leading to the proposal of a dual-network
model called the Siamese Reconstruction-Segmentation Network (SRSNet). The
proposed model is not only a universal network but also enhances the
segmentation performance without altering its structure, leveraging the
reconstruction task. Additionally, as the amount of training data for the
reconstruction network increases, the performance of the segmentation network
also improves synchronously. On seven datasets including five medical datasets
and two infrared image datasets, our SRSNet consistently achieves the best
segmentation results. The code is released at https://github.com/fidshu/SRSNet.