Fig. 2.

Our proposed pipeline (SOSS) for occlusion segmentation. The input image is fed to both the top branch and bottom branch simultaneously. Top branch: our DeepLabV3+-based network segments the input image and produces a semantic (occlusion) mask proposal. Bottom branch: the SAM-based generator produces a partitioning of the input image without corresponding semantic labels. Both kinds of masks are then fused using our proposed confidence-based soft voting mechanism for the final occlusion segmentation mask. This aims to add semantic information to the SAM branch while simultaneously improving the segmentation quality of the DeepLab branch.