SSR: Similarity-Shift Refinement for Training-Free Object-Centric Masks

• URL: http://arxiv.org/abs/2608.01103v1
• Date: Sun, 02 Aug 2026 09:06:06 GMT
• Title: SSR: Similarity-Shift Refinement for Training-Free Object-Centric Masks
• Abstract summary: Similarity-Shift Refinement improves object-centric masks with a frozen self-supervised Vision Transformer.<n> SSR measures changes in pairwise patch similarity before and after self-attention value aggregation.<n>It propagates the initial soft slot assignments in a single refinement step, without retraining or modifying either model.
• Score: 0.6302369456012739
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Object-centric models often produce fragmented masks, boundary leakage, and incorrect region merging. We introduce Similarity-Shift Refinement (SSR), a training-free post-hoc method for improving object-centric masks with a frozen self-supervised Vision Transformer. SSR measures changes in pairwise patch similarity before and after self-attention value aggregation, retains positively strengthened relations, and constructs a sparse affinity graph. This graph propagates the initial soft slot assignments in a single refinement step, without retraining or modifying either model. Across natural-image, synthetic-video, and real-world-video benchmarks, SSR improves all-pixel Adjusted Rand Index in all 24 evaluated model-dataset combinations, with an average gain of 8.5 percentage points. Ablations show that value-space similarity shifts outperform query- and key-space variants as well as static Transformer affinities. However, texture-dense scenes may cause visually similar regions to be over-grouped. Overall, SSR provides a simple and transferable signal for training-free object-centric mask refinement.

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