Pith Number

pith:BK5HDFY3

pith:2026:BK5HDFY3NICOR66XFTVJWXFPHX

not attested not anchored not stored refs pending

Weather-Robust Cross-View Geo-Localization via Prototype-Based Semantic Part Discovery

Chi-Nguyen Tran, Dao Sy Duy Minh, Huynh Trung Kiet, Long Tran-Thanh, Nguyen Lam Phu Quy, Phu-Hoa Pham

SkyPart discovers semantic parts in drone and satellite images using competing learnable prototypes to match views despite weather and altitude changes.

arxiv:2605.11654 v2 · 2026-05-12 · cs.CV · cs.AI · cs.RO

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\usepackage{pith}
\pithnumber{BK5HDFY3NICOR66XFTVJWXFPHX}

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Record completeness

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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

At 26.95M parameters and 22.14 GFLOPs, SkyPart is the smallest among top-performing methods and sets a new state of the art on SUES-200, University-1652, and DenseUAV under a single-pass, no-re-ranking, no-TTA protocol. Its advantage over the strongest baseline widens under the ten-condition WeatherPrompt corruption benchmark.

C2weakest assumption

That single-pass cosine assignment of patches to learnable prototypes will reliably discover semantic parts that separate layout from texture across the drastic view gap, and that altitude-conditioned modulation applied only during training will produce an altitude-invariant embedding at inference without loss of discriminative power.

C3one line summary

SkyPart uses learnable prototypes for patch grouping, altitude modulation only in training, graph-attention readout, and Kendall-weighted loss to set new state-of-the-art single-pass performance on SUES-200, University-1652, and DenseUAV while widening gains under weather corruptions.

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:05:47.110496Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

0aba71971b6a04e8fbd72cea9b5caf3df67e50772bc8984b8f47dc6fc8df0215

Aliases

arxiv: 2605.11654 · arxiv_version: 2605.11654v2 · doi: 10.48550/arxiv.2605.11654 · pith_short_12: BK5HDFY3NICO · pith_short_16: BK5HDFY3NICOR66X · pith_short_8: BK5HDFY3

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/BK5HDFY3NICOR66XFTVJWXFPHX \
  | 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: 0aba71971b6a04e8fbd72cea9b5caf3df67e50772bc8984b8f47dc6fc8df0215

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "b439ca3c769a0cf0648d3c7b5e67369d9441920e0e78313ad95f12080d6c0277",
    "cross_cats_sorted": [
      "cs.AI",
      "cs.RO"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-12T07:15:52Z",
    "title_canon_sha256": "9056aa31ef394b96d1d0b83a32b2682fd6187f0a10c6565016eabf5b2a7198d0"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.11654",
    "kind": "arxiv",
    "version": 2
  }
}