Pith Number

pith:7FPRI5EM

pith:2026:7FPRI5EM2S2KAUA5NIKSTEPK2V

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Learning with Semantic Priors: Stabilizing Point-Supervised Infrared Small Target Detection via Hierarchical Knowledge Distillation

Long Ma, Ping Qian, Weimin Wang, Yuanhang Yao, Zhu Liu

A frozen vision foundation model supplies semantic priors to stabilize point-supervised infrared small target detection through hierarchical distillation.

arxiv:2605.14346 v1 · 2026-05-14 · cs.CV

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4 Citations open

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Claims

C1strongest claim

We propose a hierarchical VFM-driven knowledge distillation framework that uses a frozen Vision Foundation Model (VFM) during training... Experiments on diverse challenging cases across multiple ISTD backbones demonstrate consistent improvements in detection accuracy and training stability.

C2weakest assumption

That a frozen general-purpose VFM can reliably supply semantic priors transferable to infrared small targets via SCAM modulation and bilevel optimization without domain-specific adaptation or overfitting to the training distribution.

C3one line summary

A hierarchical VFM-driven knowledge distillation method with semantic-conditioned modulation and cluster reweighting stabilizes point-supervised infrared small target detection and improves accuracy.

References

23 extracted · 23 resolved · 2 Pith anchors

[1] Anal- ysis of new top-hat transformation and the application for infrared dim small target detection.Pattern Recognition, 43(6):2145–2156, 2010

[2] A local contrast method for small infrared target detection.IEEE Transactions on Geoscience and Remote Sensing, 52(1):574–581, 2014

[3] Asymmetric contextual modulation for infrared small target detection 2021

[4] Segment Anything 2023

[5] Dense nested attention network for infrared small target detection.IEEE Transactions on Image Processing, 32:1745–1758, 2023

Receipt and verification

First computed	2026-05-17T23:39:08.126535Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

f95f14748cd4b4a0501d6a152991ead57215fd15f26aa6709dff94411c0f7970

Aliases

arxiv: 2605.14346 · arxiv_version: 2605.14346v1 · doi: 10.48550/arxiv.2605.14346 · pith_short_12: 7FPRI5EM2S2K · pith_short_16: 7FPRI5EM2S2KAUA5 · pith_short_8: 7FPRI5EM

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/7FPRI5EM2S2KAUA5NIKSTEPK2V \
  | 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: f95f14748cd4b4a0501d6a152991ead57215fd15f26aa6709dff94411c0f7970

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "1d51feb6af4f80b86f7da916ee49def1894888b390c06b8f0d005e9b1e1aca8e",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-14T04:12:08Z",
    "title_canon_sha256": "4afb830fda38fe75a4e703f92785496841b75ebbef869effa29e5d5703c469e7"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.14346",
    "kind": "arxiv",
    "version": 1
  }
}