Image segmentation without labeled data using eigenvectors with deep-spectral-segmentation
Image segmentation without labeled data using eigenvectors with deep-spectral-segmentation
Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization
Unsupervised localization and segmentation ... involve decomposing an image into semantically-meaningful segments without any labeled data.
... in an unsupervised setting ... difficulty and cost of obtaining dense image annotations ... existing unsupervised approaches struggle with complex scenes containing multiple objects.
... examine the eigenvectors of the Laplacian of a feature affinity matrix from self-supervised networks.
... find ... eigenvectors ... decompose an image into meaningful segments, and can ... localize objects ... Furthermore, by clustering the features ... can obtain well-delineated, nameable regions, i.e. semantic segmentations.
... simple spectral method outperforms the state-of-the-art in unsupervised localization and segmentation by a significant margin.
Furthermore, ... method can be readily used for a variety of complex image editing tasks, such as background removal and compositing.
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