pith:S2FQKJNZ
VFM$^{4}$SDG: Unveiling the Power of VFMs for Single-Domain Generalized Object Detection
A frozen vision foundation model supplies stability priors that cut missed detections in single-domain object detectors facing unseen conditions.
arxiv:2604.21502 v2 · 2026-04-23 · cs.CV
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Claims
Extensive experiments show that the proposed method consistently outperforms existing SOTA methods on standard SDGOD benchmarks and two mainstream DETR-based detectors, demonstrating its effectiveness, robustness, and generality.
That the analytical finding of missed-detection dominance and reduced relational stability generalizes beyond the tested conditions, and that a frozen VFM supplies transferable stability priors without introducing new failure modes in the detector.
VFM⁴SDG uses a frozen vision foundation model to inject cross-domain stability priors into both the encoding and decoding stages of object detectors, reducing missed detections in unseen environments.
Receipt and verification
| First computed | 2026-05-25T02:02:15.632590Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
968b0525b980d171ce9fd5930349e0448372309d3051ddafbfb3e4b5cb8dfecf
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/S2FQKJNZQDIXDTU72WJQGSPAIS \
| 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())"
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Canonical record JSON
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