Pith Number

pith:FHWHZML6

pith:2026:FHWHZML6FV7RRY5W26MPAWXB52

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UniTriGen: Unified Triplet Generation of Aligned Visible-Infrared-Label for Few-Shot RGB-T Semantic Segmentation

Chen Ding, Fei Zhou, Haoyu Wang, Lei Zhang, Mengmeng Zheng, Ping Zhou, Wei Wei

A single diffusion process in shared latent space generates aligned visible-infrared-label triplets from limited real pairs.

arxiv:2605.14626 v1 · 2026-05-14 · cs.CV

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\usepackage{pith}
\pithnumber{FHWHZML6FV7RRY5W26MPAWXB52}

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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

UniTriGen generates high-quality aligned triplets from limited real paired data, thereby achieving consistent performance improvements across various RGB-T semantic segmentation models.

C2weakest assumption

That jointly encoding VIS, IR, and Label into a shared latent space and modeling them with a single diffusion process will reliably enforce global cross-modal consistency in spatial structure and semantics without introducing artifacts or biases.

C3one line summary

UniTriGen uses unified diffusion in a shared latent space plus lightweight adapters and scene-balanced sampling to produce high-quality aligned VIS-IR-Label triplets from limited paired data, improving few-shot RGB-T semantic segmentation.

References

41 extracted · 41 resolved · 4 Pith anchors

[1] Textssr: diffusion-based data synthesis for scene text recognition 2025

[2] Text2earth: Unlocking text-driven remote sensing image generation with a global-scale dataset and a foundation model.IEEE Geoscience and Remote Sensing Magazine, 2025 2025

[3] Datasetdm: Synthesizing data with perception annotations using diffusion models 2023

[4] Dataset diffusion: Diffusion-based synthetic data generation for pixel-level semantic segmentation.Advances in Neural Information Processing Systems, 36: 76872–76892, 2023 2023

[5] Pseudo-sd: pseudo controlled stable diffusion for semi-supervised and cross-domain semantic segmentation 2025

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-17T23:39:04.015768Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

29ec7cb17e2d7f18e3b6d798f05ae1eeb407eb8ef040d9b723378d5275e82028

Aliases

arxiv: 2605.14626 · arxiv_version: 2605.14626v1 · doi: 10.48550/arxiv.2605.14626 · pith_short_12: FHWHZML6FV7R · pith_short_16: FHWHZML6FV7RRY5W · pith_short_8: FHWHZML6

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/FHWHZML6FV7RRY5W26MPAWXB52 \
  | 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: 29ec7cb17e2d7f18e3b6d798f05ae1eeb407eb8ef040d9b723378d5275e82028

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "6237492e6b519623a95c253ad308914b6a7e7f91d2b5bfc1c25d46347b1a437f",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-14T09:39:53Z",
    "title_canon_sha256": "ebafd982cd26bee767dc5be0b95b2e45ab935b1bbd0b7a960eb10d1d2ee37ca6"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.14626",
    "kind": "arxiv",
    "version": 1
  }
}