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.

翻译：本文提出了一种高性能的深度神经网络用于弱目标图像分割，包括医学图像分割和红外图像分割。为此，本研究分析了现有的动态卷积方法，并提出动态参数卷积（DPConv）。此外，从DPConv的角度重新评估了重建任务与分割任务之间的关系，进而提出了一种名为孪生重建-分割网络（SRSNet）的双网络模型。该模型不仅是一种通用网络，还能在不改变其结构的情况下，利用重建任务提升分割性能。同时，随着重建网络训练数据量的增加，分割网络的性能也会同步提升。在包含五个医学数据集和两个红外图像数据集的七个数据集上，我们的SRSNet始终取得了最佳的分割结果。代码已发布于https://github.com/fidshu/SRSNet。