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ProAlign: Progressive Positional and Prototype-Guided Alignment for Aerial-Ground Person Re-Identification — arXiv:2611.04980

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.04980
License (as archived): CC0-1.0
Archived: 2026-11-04T19:04:49.353Z
SHA-256 of the archived text: 347ada92f7d532ae83332010a8b375ff0e5ade3aafa9ac78dec97f1fbc3485d6
Stable URL of this page: https://metraipolis.com/documento/dc_239acea23d01

How to cite

ProAlign: Progressive Positional and Prototype-Guided Alignment for Aerial-Ground Person Re-Identification — arXiv:2611.04980 — arXiv (en), https://arxiv.org/abs/2611.04980 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_239acea23d01 (Metraipolis archive dc_239acea23d01, sha256 347ada92f7d532ae83332010a8b375ff0e5ade3aafa9ac78dec97f1fbc3485d6)

BibTeX

@misc{dc239acea23d01,
  title = {ProAlign: Progressive Positional and Prototype-Guided Alignment for Aerial-Ground Person Re-Identification — arXiv:2611.04980},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.04980},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_239acea23d01; sha256 347ada92f7d532ae83332010a8b375ff0e5ade3aafa9ac78dec97f1fbc3485d6},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.04980. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Aerial-Ground Person Re-Identification (AG-ReID) aims to match pedestrian images captured by unmanned aerial vehicles (UAVs) and ground-based surveillance cameras. The task remains highly challenging due to severe viewpoint discrepancies, frequent occlusions, and substantial domain gaps between aerial and ground imagery. Beyond these observable factors, existing methods often overlook two fundamental structural issues: cumulative positional drift during hierarchical feature extraction and semantic inconsistency across cross-view feature distributions. To address these challenges, we propose ProAlign, a Progressive Positional and Prototype-Guided Alignment Network for AG-ReID. ProAlign comprises two key components: a Layer-wise Progressive Positional Embedding (LPPE) module and a View-Aware Prototype Contrastive Learning (PCL) module. LPPE performs adaptive positional calibration throughout transformer layers by employing conditional positional encoding in shallow layers to mitigate local spatial distortions, while introducing learnable static positional embeddings in deeper layers to reinforce global semantic priors. Meanwhile, PCL maintains view-specific aerial and ground prototypes for each identity and enforces cross-view semantic consistency via a dual-prototype contrastive objective. Extensive experiments on CARGO and two real-world AG-ReID benchmarks demonstrate the effectiveness of ProAlign, achieving state-of-the-art overall retrieval performance on CARGO with improvements of 1.60% in Rank-1 accuracy and 5.01% in mAP over the strongest competing methods.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.CV, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2611.04980 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-04T18:39:37.561Z dalla pagina https://arxiv.org/list/cs.CV/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).