DEPTH IS ALL YOU NEED: SINGLE-STAGE WEAKLY SUPERVISED SEMANTIC SEGMENTATION FROM IMAGE-LEVEL SUPERVISION

Date

2022-01-01

Author

Ergül, Mustafa
Alatan, Abdullah Aydın

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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 image-level 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 more complex pipelines and outperforms SOTA single-stage methods.

Subject Keywords

Depth, Self supervision, Semantic segmentation, Single stage, Weakly supervision

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https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85146723922&origin=inward
https://hdl.handle.net/11511/107569

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Department of Electrical and Electronics Engineering, Conference / Seminar