The realm of Weakly Supervised Instance Segmentation (WSIS) under box
supervision has garnered substantial attention, showcasing remarkable
advancements in recent years. However, the limitations of box supervision
become apparent in its inability to furnish effective information for
distinguishing foreground from background within the specified target box. This
research addresses this challenge by introducing pseudo-depth maps into the
training process of the instance segmentation network, thereby boosting its
performance by capturing depth differences between instances. These
pseudo-depth maps are generated using a readily available depth predictor and
are not necessary during the inference stage. To enable the network to discern
depth features when predicting masks, we integrate a depth prediction layer
into the mask prediction head. This innovative approach empowers the network to
simultaneously predict masks and depth, enhancing its ability to capture
nuanced depth-related information during the instance segmentation process. We
further utilize the mask generated in the training process as supervision to
distinguish the foreground from the background. When selecting the best mask
for each box through the Hungarian algorithm, we use depth consistency as one
calculation cost item. The proposed method achieves significant improvements on
Cityscapes and COCO dataset.

本研究通过引入伪深度图到实例分割网络的训练过程中，捕捉实例间的深度差异，从而提升性能；同时，利用深度一致性作为选择最佳掩码的计算成本，使得该方法在实例分割中取得显著改进。