Boosting Unsupervised Semantic Segmentation with Principal Mask Proposals
![No Thumbnail [100%x80]](data:image/svg+xml;base64,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)
Description
Unsupervised semantic segmentation aims to automatically partition images into semantically meaningful regions by identifying global semantic categories within an image corpus without any form of annotation. Building upon recent advances in self-supervised representation learning, we focus on how to leverage these large pre-trained models for the downstream task of unsupervised segmentation. We present PriMaPs – Principal Mask Proposals – decomposing images into semantically meaningful masks based on their feature representation. This allows us to realize unsupervised semantic segmentation by fitting class prototypes to PriMaPs with a stochastic expectation-maximization algorithm, PriMaPs-EM. Despite its conceptual simplicity, PriMaPs-EM leads to competitive results across various pre-trained backbone models, including DINO and DINOv2, and across different datasets, such as Cityscapes, COCO-Stuff, and Potsdam-3. Importantly, PriMaPs-EM is able to boost results when applied orthogonally to current state-of-the-art unsupervised semantic segmentation pipelines.
DFG subject classification
4.43-04 Künstliche Intelligenz und Maschinelle LernverfahrenRelated third party funded projects
EC/H2020 | 866008 | REDHMWK | 500/10.001-(00111) | 3AI - TP Roth
HMWK | 500/10.001-(00012) | TAM - TP Roth
HMWK | 500/10.001-(00111) | 3AI-NWG Schaub-Meyer
Related Resources
- Is described by: arXiv:https://arxiv.org/abs/2404.16818
- Is described by: https://openreview.net/forum?id=UawaTQzfwy
- Is described by: https://github.com/visinf/primaps
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Segmentation [8]
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Except where otherwise noted, this item's license is described as Apache License 2.0