dtourolle
edf19ab798
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.
71 lines
2.1 KiB
YAML
71 lines
2.1 KiB
YAML
site_name: scene-actor-extraction
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site_url: https://pages.tourolle.paris/dtourolle/scene-actor-extraction/
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site_description: Face-recognition pipeline for finding on-screen actor presence in film/TV, built on KPN++
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repo_url: https://gitea.tourolle.paris/dtourolle/scene-actor-extraction
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repo_name: dtourolle/scene-actor-extraction
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- Home: index.md
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- How We Score Against X-Ray: methodology.md
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- Learned Scene-Boundary Detector: scene-boundary-detector.md
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- Benchmark — SuperHero: benchmark.md
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- Full Experiment Log: model-bakeoff.md
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- Service Conversion (proposal): service-conversion.md
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- Archive (July 2026):
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- How We Scored (July): methodology-2026-07.md
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- Best Model: best-model-2026-07.md
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- Gallery Scope (Full vs. Limited): gallery-scope-2026-07.md
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- Pose Expansion: pose-expansion-2026-07.md
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- LVFace Deep Dive: lvface-deep-dive-2026-07.md
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- Full Experiment Log (July): model-bakeoff-2026-07.md
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