Not All Confusion Is Equal: A Source-Aware Uncertainty Diagnosis for Fine-Grained Aircraft Detection

arXiv:2609.29959 · cs.CV, cs.LG · Submitted 2026-09-24 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-09-24

Updated: 2026-09-24

Terminology

• Epistemic Uncertainty — 6× in this paper · explained in 10 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Aleatoric Uncertainty — 2× in this paper · explained in 5 episodes
• Uncertainty Quantification — 2× in this paper · explained in 8 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Active Learning — 1× in this paper · explained in 6 episodes
• Bayesian Framework — 1× in this paper · explained in 3 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Mechanistic interpretability — 1× in this paper · explained in 2 episodes
• Steering — 1× in this paper · explained in 2 episodes

Sources

• Bayesian Active Learning for Classification and Preference Learning
• Not Just How Much, But Where: Decomposing Epistemic Uncertainty into Per-Class Contributions

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models