Single-stage Semantic Segmentation from Image Labels

Araslanov, Nikita; Roth, Stefan

Open Access

Single-stage Semantic Segmentation from Image Labels

Files

1-stage-wseg-code.zip (Training and inference code (PyTorch)) (6.9 MB)

snapshots.zip (Model parameters (snapshots)) (862.39 MB)

results.zip (Inference results) (4.08 GB)

Date

2021-12-22

Type

Software

Authors

Araslanov, Nikita
Roth, Stefan

Description

Recent years have seen a rapid growth in new approaches improving the accuracy of semantic segmentation in a weakly supervised setting, i.e. with only image-level labels available for training. However, this has come at the cost of increased model complexity and sophisticated multi-stage training procedures. This is in contrast to earlier work that used only a single stage − training one segmentation network on image labels − which was abandoned due to inferior segmentation accuracy. In this work, we first define three desirable properties of a weakly supervised method: local consistency, semantic fidelity, and completeness. Using these properties as guidelines, we then develop a segmentation-based network model and a self-supervised training scheme to train for semantic masks from image-level annotations in a single stage. We show that despite its simplicity, our method achieves results that are competitive with significantly more complex pipelines, substantially outperforming earlier single-stage methods.