[Submitted on 10 Sep 2026]
Title:Feature Recovery for Object Understanding After Irreversible Fire Damage
View a PDF of the paper titled Feature Recovery for Object Understanding After Irreversible Fire Damage, by Aditi Tiwari and 4 other authors
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Abstract: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.
Comments: 28 pages, 11 figures, 9 tables
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
ACM classes: I.2.10; I.4.8
Cite as: arXiv:2609.12078 [cs.CV]
(or arXiv:2609.12078v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.12078
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Aditi Tiwari [view email] [v1] Thu, 10 Sep 2026 18:07:01 UTC (7,743 KB)
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