make_figures.py gains the DE-landscape, calibration, and per-film leave-one-out
holdout figures used by the experiment log. regen_frame_examples.sh replays all
nine films with the shipped learned-boundary flood config and draws GT-aware
TP/FP/FN frames, so every annotated image in the docs is reproducible.
New docs/scene-boundary-detector.md: why the grayscale cut detector wasn't
enough (Scarface: 1 cut in 10k frames → flood-fill P=26%), what X-Ray boundaries
are and why they're hard, the feature/model design (delta histograms, multi-scale
ramp bank, scene-length debounce, soft-target XGBoost regressor, per-film knee),
and the measured dead ends (audio-only, raw features, LSTM, TransNetV2).
Headline result, honest leave-one-out (each film scored by a detector trained on
the other eight): flood + learned detector = 74.9% macro presence F1, vs 64.0%
for grayscale-cut flood and 62.6% for track-extent — +12.3pp, improving all nine
films. Fixes the Scarface flood collapse (grayscale 40.9 → learned 74.9, on a
film the detector never trained on) and swings Downton +37pp.
Figures are generated by scripts/scene_detector/make_figures.py from the saved
results (experiments/results/scene_boundary/downstream_loo.json); the PNGs
themselves follow the repo convention of not committing regenerable chart assets.
Added to the mkdocs nav.
dtourolle