pith. sign in
Pith Number

pith:QMGYHQKE

pith:2026:QMGYHQKELGSPQEOBCQRAXKELMF
not attested not anchored not stored refs resolved

RIDE: Retinex-Informed Decoupling for Exposing Concealed Objects

Chengyu Fang, Chunming He, Dingming Zhang, Fengyang Xiao, Jingjia Feng, Longxiang Tang, Rihan Zhang, Sina Farsiu

Retinex decomposition separates illumination from reflectance to widen the gap between foreground and background in concealed object segmentation.

arxiv:2605.15450 v1 · 2026-05-14 · cs.CV · cs.AI · cs.LG

Add to your LaTeX paper
\usepackage{pith}
\pithnumber{QMGYHQKELGSPQEOBCQRAXKELMF}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp
2 Internet Archive
3 Author claim open · sign in to claim
4 Citations open
5 Replications open
Portable graph bundle live · download bundle · merged state
The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Across diverse COS sub-tasks, the underlying physical processes systematically anti-correlate illumination and reflectance differences, yielding theoretical guarantees that Retinex decomposition preserves or strictly improves total foreground--background discriminability across the full physical regime, with anti-correlation maximizing the gain.

C2weakest assumption

The assumption that physical processes in COS tasks (camouflage, polyps, transparent objects, defects) systematically anti-correlate illumination and reflectance differences, as invoked to support the Discriminability Gap Theorem and the claimed guarantees.

C3one line summary

RIDE applies Retinex-based homogeneous decomposition to improve foreground-background discriminability in concealed object segmentation tasks across multiple domains.

References

73 extracted · 73 resolved · 2 Pith anchors

[1] Camouflaged object detection 2020
[2] Anabranch network for camouflaged object segmentation.Comput 2019
[3] Pranet: Parallel reverse attention network for polyp segmentation 2020
[4] Scaler: Sam-enhanced collaborative learning for label-deficient concealed object segmentation.arXiv preprint arXiv:2511.18136, 2025 2025
[5] Refining context-entangled content segmentation via curriculum selection and anti-curriculum promotion.arXiv preprint arXiv:2602.01183, 2026 2026

Formal links

2 machine-checked theorem links

Receipt and verification
First computed 2026-05-20T00:00:59.289345Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

830d83c14459a4f811c114220ba88b61697521148514d01d35e3e551313b0d84

Aliases

arxiv: 2605.15450 · arxiv_version: 2605.15450v1 · doi: 10.48550/arxiv.2605.15450 · pith_short_12: QMGYHQKELGSP · pith_short_16: QMGYHQKELGSPQEOB · pith_short_8: QMGYHQKE
Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/QMGYHQKELGSPQEOBCQRAXKELMF \
  | 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: 830d83c14459a4f811c114220ba88b61697521148514d01d35e3e551313b0d84
Canonical record JSON
{
  "metadata": {
    "abstract_canon_sha256": "273991abc078b20cab6aee96aae766b332a303c9767ed9e34dec3a22d8defda6",
    "cross_cats_sorted": [
      "cs.AI",
      "cs.LG"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-14T22:19:52Z",
    "title_canon_sha256": "4d48ebee0c0bc1a41753b711a5c413680d737327ee594faae017e1e7d8e7a653"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.15450",
    "kind": "arxiv",
    "version": 1
  }
}