Aperture: Training-Free Multiscale Concept Bottlenecks for Remote Sensing

arXiv:2609.38603 · cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.CV

Submitted: 2026-09-29

Updated: 2026-09-29

Terminology

• Aperture — 41× in this paper · explained in 1 episode
• Verification — 19× in this paper · explained in 2 episodes
• Benchmark — 10× in this paper · explained in 2 episodes
• Interpretability — 9× in this paper · explained in 15 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• RAG — 5× in this paper · explained in 3 episodes
• Conference on Computer Vision and Pattern Recognition — 4× in this paper
• Vision-language models — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• EMMA — 3× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• SATELLITE IMAGERY FACILITY CONCEPTS — 2× in this paper
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Anusandhan National Research Foundation — 1× in this paper
• CVF International Conference on Computer Vision — 1× in this paper
• Explainability — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Optimal Transport — 1× in this paper · explained in 5 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Training-Free Framework — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Winter Conference on Applications of Computer Vision — 1× in this paper

Sources

• Calibrated Large Language Models for Binary Question Answering
• Language Models (Mostly) Know What They Know
• Gemma 4 Technical Report
• Zero-shot Concept Bottleneck Models

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