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。

利用伪深度增强以盒子为监督的实例分割

Boosting Box-supervised Instance Segmentation with Pseudo Depth

In stereo-matching knowledge distillation methods of the self-supervised
monocular depth estimation, the stereo-matching network's knowledge is
distilled into a monocular depth network through pseudo-depth maps. In these
methods, the learning-based stereo-confidence network is generally utilized to
identify errors in the pseudo-depth maps to prevent transferring the errors.
However, the learning-based stereo-confidence networks should be trained with
ground truth (GT), which is not feasible in a self-supervised setting. In this
paper, we propose a method to identify and filter errors in the pseudo-depth
map using multiple disparity maps by checking their consistency without the
need for GT and a training process. Experimental results show that the proposed
method outperforms the previous methods and works well on various
configurations by filtering out erroneous areas where the stereo-matching is
vulnerable, especially such as textureless regions, occlusion boundaries, and
reflective surfaces.

本研究提出了一种基于多个视差图来检查一致性的方法，以识别并过滤伪深度图中的错误，无需真实值（GT）和训练过程，并通过消除立体匹配易受攻击的错误区域，如无纹理区域、遮挡边界和反射表面，取得了比以往方法更好的实验结果。