Boosting Box-supervised Instance Segmentation with Pseudo Depth
Xinyi Yu Ling Yan Pengtao Jiang Hao Chen Bo Li Lin Yuanbo Wu Linlin Ou
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.