arXiv Computer Vision
Feature Recovery for Object Understanding After Irreversible Fire Damage
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arXiv Computer Vision
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Hard, Yet Reducible: Controlled Forward Transfer for Synthetic Degradation Curation
arXiv:2610.09849v1 Announce Type: new Abstract: Selecting synthetic degradations for dense prediction requires an estimate of their training utility, the generalization gain they bring under a finite...
arXiv Machine Learning
Oct 12
Shortcut Learning in Geospatial Foundation Model Embeddings for Cross-Disaster Damage Assessment
The study investigates shortcut learning in geospatial foundation model embeddings for cross-disaster damage assessment. By holding out entire disasters during evaluation, the authors demonstrate a significant drop in mean balanced accuracy across multiple datasets, revealing that models rely on event or scene characteristics rather than transferable evidence. They further show that simple feature-level transformations, such as supervised contrastive learning or PCA, can improve generalization to unseen disasters.
Hugging Face Trending Papers
Oct 7
Hard, Yet Reducible: Controlled Forward Transfer for Synthetic Degradation Curation
Selecting synthetic degradations for dense prediction requires an estimate of their training utility, the generalization gain they bring under a finite training budget. Clean and degraded twins share...
arXiv AI
Sep 30
Embedded Bi-Temporal Building Damage Assessment for On-Board Data Reduction
arXiv:2609.37013v1 Announce Type: cross Abstract: Rapid assessment of building damage after natural disasters is essential to support emergency response. Earth Observation satellites can acquire rele...
arXiv AI
Sep 10
SAFIRE: Safety-Critical Benchmark for Fine-grained Fire and Smoke Understanding in Multimodal LLMs
SAFIRE is a large-scale benchmark for fire and smoke understanding in multimodal large language models (MLLMs), featuring 83,000 captioned images across 20 scenarios and 193,000 multiple-choice VQA questions derived from a 9.7K-image subset. The benchmark evaluates 10 dimensions of performance, from basic perception to higher-order reasoning, and employs a GPT‑5.4-assisted verification pipeline to ensure annotation quality. Experiments on ten open-source MLLMs (8B–38B) reveal an average accuracy of 61.9%, highlighting significant gaps in safety-critical reasoning, while fine-tuning vision encoders on just 7% of SAFIRE data boosts fire-scene classification accuracy from 20.1% to 64.5%. All resources are publicly available at https://risys-lab.github.io/SAFIRE/.
arXiv Computer Vision
Sep 15
Restore What Matters: Lessons from Joint Restoration and Recognition
arXiv:2609.13791v1 Announce Type: new Abstract: Recognition pipelines typically adopt a restore-then-recognize workflow, yet decades of experience show that generating visually pleasing images seldom...