We introduce a novel bilateral reference framework (***BiRefNet***) for high-resolution dichotomous image segmentation (DIS). It comprises two essential components: the localization module (LM) and the reconstruction module (RM) with our proposed bilateral reference (BiRef). The LM aids in object localization using global semantic information. Within the RM, we utilize BiRef for the reconstruction process, where hierarchical patches of images provide the source reference and gradient maps serve as the target reference. These components collaborate to generate the final predicted maps. We also introduce auxiliary gradient supervision to enhance focus on regions with finer details. Furthermore, we outline practical training strategies tailored for DIS to improve map quality and training process. To validate the general applicability of our approach, we conduct extensive experiments on four tasks to evince that *BiRefNet* exhibits remarkable performance, outperforming task-specific cutting-edge methods across all benchmarks.

我们引入了一种新的双边参考框架（BiRefNet），用于高分辨率的二分图像分割（DIS），通过全局语义信息的方式来进行对象定位，利用双边参考进行图像重建，并引入辅助梯度监督来提高对细节区域的关注，这种方法在多个任务中展现非凡的性能，超过了各种基准上的最新方法。

高分辨率二鉴图像分割的双边参考