17 lines
2.4 KiB
Markdown
17 lines
2.4 KiB
Markdown
# Manuscript outline — evidence gate, not a submission-ready paper
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Working title: Matching-Aware Training and Finite-Sample Object-Level False-Alarm Calibration for Infrared Small Target Detection.
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1. **Introduction:** operational mismatch between pixel overlap and object alarms. State measurable question: does matching-aware gate training improve recall under the same certified expected count budget? No first-ever graph/loss/calibration claims.
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2. **Related work:** SLS/SD/AC-SLSIoU/CG-IRNet; MDvsFA; graph candidate filtering; NeurIPS metric study; Neyman–Pearson learning; LTT/CRC. Explicitly distinguish segmentation, box detection and subpixel unmixing.
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3. **Problem:** candidate generation, one-to-one matching, FP/image versus P(any FA), distribution and sampling assumptions, immutable calibration family.
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4. **Method:** compact backbone, frozen candidate masks, canonical assignment, differentiable count upper bound, size-weighted positive loss and projected dual training. Explain why MLP is the first implementation; graph features only if ablation supports them.
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5. **Analysis:** unsafe conflict-edge counterexample, pixel splitting counterexample, matching-deficiency monotonicity, surrogate bound, tail-sum certificate, sample complexity and distribution-shift limits. Credit established statistical tools.
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6. **Experiments:** audited datasets and disjoint groups; all baselines get same calibration; three seeds; useful-certificate rate and recall; count budgets; real negative scenes; close pairs; cross-dataset transfer; latency/memory.
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7. **Failure analysis:** missed proposals, merged targets, canonical-label conservatism, sample scarcity, empty-output certificates, correlated exposures, annotation uncertainty.
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8. **Conclusion:** only claims supported by results; no assumed SOTA improvement.
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Planned figures: object-versus-pixel threshold counterexample; method schematic; held-out Pd versus FP/image curves with calibrated points; sample-size/abstention curve; failure montage. Planned tables: prior-art distinctions; main comparisons at predeclared budgets; ablations; hardware costs. All result cells remain blank until actual runs.
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Submission gate: full-text novelty audit, verified upstream baselines, nontrivial calibrated performance on real data, repeated tests, reproducible artifacts and an independent checking pass. The current package does not pass that gate.
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