tiny_object_detection/docs/manuscript_outline.md

Manuscript outline — evidence gate, not a submission-ready paper

Working title: Matching-Aware Training and Finite-Sample Object-Level False-Alarm Calibration for Infrared Small Target Detection.

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.
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.
3. Problem: candidate generation, one-to-one matching, FP/image versus P(any FA), distribution and sampling assumptions, immutable calibration family.
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.
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.
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.
7. Failure analysis: missed proposals, merged targets, canonical-label conservatism, sample scarcity, empty-output certificates, correlated exposures, annotation uncertainty.
8. Conclusion: only claims supported by results; no assumed SOTA improvement.

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.

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.