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PriCBMs: Multi-granular Concept Bottleneck Vision-Language Models for Visual Privacy Recognition — arXiv:2611.02661

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.02661
License (as archived): CC0-1.0
Archived: 2026-11-03T18:55:05.243Z
SHA-256 of the archived text: d7b53331002e2f2d521fb952e141be5f25552d942b1e32793618dabff3ab1e57
Stable URL of this page: https://metraipolis.com/documento/dc_e3efbf488dea

How to cite

PriCBMs: Multi-granular Concept Bottleneck Vision-Language Models for Visual Privacy Recognition — arXiv:2611.02661 — arXiv (en), https://arxiv.org/abs/2611.02661 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_e3efbf488dea (Metraipolis archive dc_e3efbf488dea, sha256 d7b53331002e2f2d521fb952e141be5f25552d942b1e32793618dabff3ab1e57)

BibTeX

@misc{dce3efbf488dea,
  title = {PriCBMs: Multi-granular Concept Bottleneck Vision-Language Models for Visual Privacy Recognition — arXiv:2611.02661},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.02661},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_e3efbf488dea; sha256 d7b53331002e2f2d521fb952e141be5f25552d942b1e32793618dabff3ab1e57},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.02661. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Widespread image sharing on social media has increased the need for visual privacy recognition to identify inadvertently exposed privacy information. However, multi-label visual privacy recognition presents two challenges. First, privacy categories represent high-level semantic attributes and do not have a one-to-one correspondence with visual objects. Different visual objects may involve the same privacy category, while the same type of object may contain different types of privacy-sensitive information. In this setting, direct prediction methods generally lack explicit guidance for identifying category-relevant privacy cues, leaving this process implicit in the learned image-to-label mapping and potentially limiting classification performance. Second, privacy cues are often distributed across image regions and may not be adequately represented by whole-image features. To address these challenges, we propose Privacy Concept Bottleneck Models (PriCBMs). By introducing privacy-related concepts as intermediate semantic representations, PriCBMs first identify category-relevant privacy cues through visual-concept matching and then predict privacy categories based on concept responses. This process makes privacy predictions more interpretable. To improve the quality of these concepts, we design a concept selection strategy based on target-category relevance, cross-category discriminability, and semantic diversity. Meanwhile, we introduce a global-region modeling module to capture localized privacy evidence while retaining global context. Experiments on VISPR and FineViP show that PriCBMs outperform evaluated competitors across all four multi-label metrics. Ablation studies and concept-quality analyses support the effectiveness of the proposed design. 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/2611.02661 — 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 2026-11-03T18:16:53.011Z dalla pagina https://arxiv.org/list/cs.CV/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).