Stable Single-Pixel Contrastive Learning for Semantic and Geometric Tasks

• URL: http://arxiv.org/abs/2512.04970v1
• Date: Thu, 04 Dec 2025 16:38:26 GMT
• Title: Stable Single-Pixel Contrastive Learning for Semantic and Geometric Tasks
• Authors: Leonid Pogorelyuk, Niels Bracher, Aaron Verkleeren, Lars Kühmichel, Stefan T. Radev,
• Abstract summary: Our approach maps each pixel of an image to an overcomplete descriptor that is both view-invariant and semantically meaningful.<n>It enables precise point-correspondence across images without requiring momentum-based teacher-student training.
• Score: 2.5178202810957235
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We pilot a family of stable contrastive losses for learning pixel-level representations that jointly capture semantic and geometric information. Our approach maps each pixel of an image to an overcomplete descriptor that is both view-invariant and semantically meaningful. It enables precise point-correspondence across images without requiring momentum-based teacher-student training. Two experiments in synthetic 2D and 3D environments demonstrate the properties of our loss and the resulting overcomplete representations.

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