pith:ROSGYSME
Early Semantic Grounding in Image Editing Models for Zero-Shot Referring Image Segmentation
Instruction-based image editing models show strong foreground-background separability in their earliest internal features, enabling zero-shot referring image segmentation from a single denoising step.
arxiv:2605.13122 v1 · 2026-05-13 · cs.CV
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Claims
strong foreground-background separability emerges in the internal representations of these models at the earliest denoising timestep, well before any visible image transformation occurs
The feature-space separability observed at the earliest denoising timestep is sufficient to produce accurate pixel-level segmentation masks for arbitrary referring expressions without full synthesis or task-specific training.
Pretrained instruction-based image editing models exhibit early foreground-background separability that enables a training-free framework for zero-shot referring image segmentation using a single denoising step.
References
Receipt and verification
| First computed | 2026-05-18T03:08:57.947708Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
8ba46c4984af08def11b890fe5b6cc7e350d92f1e553098186af0f177a134f38
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/ROSGYSMEV4EN54I3REH6LNWMPY \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 8ba46c4984af08def11b890fe5b6cc7e350d92f1e553098186af0f177a134f38
Canonical record JSON
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