SOURCE-LINKED INTELLIGENCE
Can Frozen Hyperspherical Features Guide the Selection of Pseudo Masks?
Foundation segmenters such as SAM return several plausible masks for an unlabeled image, and a student trained on the wrong one inherits its errors. Choosing among them means querying a second large model or fitting a quality head to annotated masks. We show that a candidate can be judged by what it does to a frozen self-supervised backbone's features. Normalized DINOv2 patch features lie on a hypersphere, and a candidate mask splits that sphere in two. Based on this reading, we introduce SphereTrust, which scores each candidate by three properties of the split, the angular contrast between th
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-24T16:30:39.000Z
First collected: 2026-09-25T06:12:46.948Z. This is not the publication date.