Learning Accurate Segmentation Purely from Self-Supervision

• URL: http://arxiv.org/abs/2602.23759v1
• Date: Fri, 27 Feb 2026 07:36:32 GMT
• Title: Learning Accurate Segmentation Purely from Self-Supervision
• Authors: Zuyao You, Zuxuan Wu, Yu-Gang Jiang,
• Abstract summary: Selfment is a fully self-supervised framework that segments objects directly from raw images without human labels.<n>Selfment sets new state-of-the-art (SoTA) results across multiple benchmarks.
• Score: 87.78965637247107
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Accurately segmenting objects without any manual annotations remains one of the core challenges in computer vision. In this work, we introduce Selfment, a fully self-supervised framework that segments foreground objects directly from raw images without human labels, pretrained segmentation models, or any post-processing. Selfment first constructs patch-level affinity graphs from self-supervised features and applies NCut to obtain an initial coarse foreground--background separation. We then introduce Iterative Patch Optimization (IPO), a feature-space refinement procedure that progressively enforces spatial coherence and semantic consistency through iterative patch clustering. The refined masks are subsequently used as supervisory signals to train a lightweight segmentation head with contrastive and region-consistency objectives, allowing the model to learn stable and transferable object representations. Despite its simplicity and complete absence of manual supervision, Selfment sets new state-of-the-art (SoTA) results across multiple benchmarks. It achieves substantial improvements on $F_{\max}$ over previous unsupervised saliency detection methods on ECSSD ($+4.0\%$), HKUIS ($+4.6\%$), and PASCAL-S ($+5.7\%$). Moreover, without any additional fine-tuning, Selfment demonstrates remarkable zero-shot generalization to camouflaged object detection tasks (e.g., $0.910$ $S_m$ on CHAMELEON and $0.792$ $F_β^ω$ on CAMO), outperforming all existing unsupervised approaches and even rivaling the SoTA fully supervised methods.

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