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DADKC: Distribution-Aware Adaptive Differentiated Knowledge Consolidation for Cloth-Hybrid Lifelong Person Re-identification — arXiv:2611.00768

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.00768
License (as archived): CC0-1.0
Archived: 2026-11-03T11:40:46.202Z
SHA-256 of the archived text: 932a303ba0fb3a1e81a919a53e6f19f1a5c0840a49f83b407484376faa3f01cb
Stable URL of this page: https://metraipolis.com/documento/dc_4db8a09ec392

How to cite

DADKC: Distribution-Aware Adaptive Differentiated Knowledge Consolidation for Cloth-Hybrid Lifelong Person Re-identification — arXiv:2611.00768 — arXiv (en), https://arxiv.org/abs/2611.00768 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4db8a09ec392 (Metraipolis archive dc_4db8a09ec392, sha256 932a303ba0fb3a1e81a919a53e6f19f1a5c0840a49f83b407484376faa3f01cb)

BibTeX

@misc{dc4db8a09ec392,
  title = {DADKC: Distribution-Aware Adaptive Differentiated Knowledge Consolidation for Cloth-Hybrid Lifelong Person Re-identification — arXiv:2611.00768},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.00768},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4db8a09ec392; sha256 932a303ba0fb3a1e81a919a53e6f19f1a5c0840a49f83b407484376faa3f01cb},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.00768. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Lifelong Person Re-identification (LReID) aims to match the same person using sequentially collected data. In practical long-term deployments, however, the model must handle both domain shifts across cameras and scenes, and irregular clothing changes of the same identity. This gives rise to the Cloth-Hybrid Lifelong Person Re-identification (CH-LReID) task, which demands balancing cloth-relevant and cloth-irrelevant knowledge in streaming data. While existing LReID methods have explored replay-based or distillation-based anti-forgetting strategies, they typically fail to sufficiently model stage-aware domain distributions and cross-stage feature continuity under exemplar-free training. To address this problem, we propose a Distribution-Aware Adaptive Differentiated Knowledge Consolidation (DADKC) framework. The core idea is to enhance the existing Differentiated Knowledge Consolidation (DKC) paradigm with adaptive distribution modeling. To this end, three lightweight modules are designed: (1) an Anchor Pool Adapter (APA) to retrieve learnable anchor cues for refining identity features; (2) a Phase Path Mixer (PPM) to perform stage-aware expert routing for integrating domain-specific distribution paths; and (3) a Stage Bridge Alignment (SBA) module to regularize the continuity of base, anchor, and path representations between adjacent stages. Extensive experiments on the CH-LReID and LReID benchmarks show that DADKC gains 4.1%/4.5% mAP/R1 on Order-1 and 3.6%/3.4% mAP/R1 on Order-2 over the DKC baseline, and lowers weighted forgetting from 2.4%/1.7% to 1.8%/1.4%, while storing neither historical images nor per-image features. The code is available at this https URL .

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/2611.00768 — 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 2026-11-03T11:06:37.445Z dalla pagina https://arxiv.org/list/cs.CV/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).