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Depth is all you Need: Single-Stage Weakly Supervised Semantic Segmentation From Image-Level Supervision by Mustafa Ergul; Aydin Alatan is a scholarly article available to read on EtoBox.

What is Depth is all you Need: Single-Stage Weakly Supervised Semantic Segmentation From Image-Level Supervision about?

The costly process of obtaining semantic segmentation labels has driven research towards to weakly supervised semantic segmentation (WSSS) methods, with only image-level labels available for training. The lack of dense semantic scene representation requires methods to increase complexity to obtain additional semantic information (i.e. object/stuff extent and boundary) about the scene. This is often done though increased model complexity and sophisticated multi-stage training/refinement procedures. However, the lack of 3D geometric structure of a single image makes these efforts desperate at a certain point. In this work, we propose to harness (inverse) depth maps estimated from one single image via a monocular depth estimation model to integrate the 3D geometric structure of the scene into the segmentation model. In light of this proposal, we develop an end-to-end segmentation-based network model and a self-supervised training process to train for semantic masks from only imagelevel annotations in a single stage. Our experiments show that our one-stage method achieves comparable segmentation performance (val: 64.32, test: 64.91) on Pascal VOC when compared with those significantly

Author

Mustafa Ergul; Aydin Alatan

Publisher

IEEE

Published

2022

Language

EN