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Feature Recovery for Object Understanding After Irreversible Fire Damage — arXiv:2609.12078

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.12078
License (as archived): CC0-1.0
Archived: 2026-10-16T03:57:28.603Z
SHA-256 of the archived text: 7eed1ed5b79d5f7168465e21e2bb855e712acb816ab96e41f26132479c561f81
Stable URL of this page: https://metraipolis.com/documento/dc_aaa7079dc8a4

How to cite

Feature Recovery for Object Understanding After Irreversible Fire Damage — arXiv:2609.12078 — arXiv (en), https://arxiv.org/abs/2609.12078 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_aaa7079dc8a4 (Metraipolis archive dc_aaa7079dc8a4, sha256 7eed1ed5b79d5f7168465e21e2bb855e712acb816ab96e41f26132479c561f81)

BibTeX

@misc{dcaaa7079dc8a4,
  title = {Feature Recovery for Object Understanding After Irreversible Fire Damage — arXiv:2609.12078},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.12078},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_aaa7079dc8a4; sha256 7eed1ed5b79d5f7168465e21e2bb855e712acb816ab96e41f26132479c561f81},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.12078. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Objects in post-fire environments often undergo irreversible physical transformations that change their geometry, material state, and visual appearance. Detecting and identifying these remnants is critical for locating hazards, reconstructing pre-incident contents, and inventorying losses. Unlike standard image corruptions, these degradations affect the physical structure of the object itself. To study this setting, we introduce TRACE, a transformation-aware benchmark for post-fire object understanding. TRACE contains 21.4K real-image-grounded synthetic scenes and paired object-level pristine-to-degraded progressions spanning 499 object identities across 189 categories. We define five tasks targeting localization and pre-degradation understanding: degraded-object detection, pristine-state recovery and retrieval, original material recovery, pristine description generation, and functional reasoning. Existing models degrade sharply with severity. From the least to the most severe level, RF-DETR mAP decreases by 71% relative, while InternVL3.5 retrieval R@1 falls from 93.85 to 28.11. To address this, we propose the Feature Recovery Module (FRM), a plug-and-play module that maps degraded encoder features to pristine-aligned representations while keeping the host frozen. Trained only with paired feature supervision, FRM improves scene-level detection, CLIP/SigLIP2 feature recovery, and all four object-level VLM tasks, with larger gains under more severe degradation. Across VLM hosts and severity levels, relative gains average 12.5% for retrieval, 20.1% for material recovery, 13.2% for description generation, and 12.4% for functional reasoning.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-14 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.12078 — 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 2026-10-16T03:44:26.214Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-14?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).