Wire the XGBoost scene-boundary detector into scene_analyze as a post-EOF step in
the result sink (like flood-fill itself — the per-film knee threshold needs the
whole film, so it cannot stream). With --scene-xgb-model set, the camera-position
node stamps a per-frame RGB histogram onto the Frame, it rides through to the
sink, and at EOF the sink runs XGBSceneBoundary over the collected histograms +
the movie's per-second audio log-PSD to produce the flood-fill boundaries. Falls
back to is_scene_boundary / is_cut when no model is configured or inference fails.
Inference is real XGBoost via CMake FetchContent (v2.1.1, static), C API in
src/inference/xgb_scene_boundary.hpp; audio log-PSD in src/inference/
audio_logpsd.hpp (FFTW + ffmpeg full-file 16kHz decode). Feature extraction
matches training exactly — video features verified row-identical to numpy, and to
avoid chasing numpy's every rounding the shipped model is TRAINED on the
C++-extracted features (scene_features_dump exe → train_xgb_cpp.py). The
C++/Python peak-finders differ slightly so boundary counts differ, but what
matters is downstream: flood + C++ detector = 75.8% macro presence F1 vs 64.0%
for the histogram-cut flood and 62.5% for track_extent, and it fixes the Scarface
flood collapse (41 -> 70). All nine films improve.
Guarded by the SAE_SCENE_XGB CMake option (on by default; heavy first build).
xgb_boundary_parity is a diff harness; scene_features_dump writes the C++ feature
matrix so training and inference share one feature implementation.
Verified end to end: scene_analyze --scene-xgb-model on a real movie stamps the
histogram, runs the detector at EOF ("XGBoost scene detector: N boundaries"), and
flood-snaps presence to the learned boundaries.
A boosted-tree scene-boundary detector that replaces the grayscale
histogram-correlation cut detector as the flood-fill boundary source, and
substantially improves actor-presence accuracy.
Downstream result (per-second X-Ray presence F1, macro over 9 films):
track_extent 62.3% | flood + histogram cuts 64.0% | flood + this 76.9%
+12.9pp, and it wins on every film — notably fixing the histogram flood's
Scarface collapse (61 -> 41 -> 71) and lifting Downton 41 -> 84.
Design (each choice measured — see the memory / report):
- XGBoost REGRESSOR on a ±3s window of DELTA features (symmetric RGB-hist
and audio-PSD deltas at k=1,2,4,8s + ramp bank + time-since-last-peak
debounce). Raw histograms dilute; deltas separate boundaries ~4-5x.
- SOFT Gaussian proximity target (sigma=10s) so near-misses train as
near-correct, not hard negatives; regression -> smooth score -> NMS peaks.
- KNEE per-film threshold: self-calibrates the boundary count to ~the true
scene count, no global rate. Evaluated at ±20s (X-Ray scenes ~170s).
- Trained on all 9 films (Cafe/Scarface low-contrast grades must be seen).
Honest held-out ~41% boundary-F1 @±20s vs ~27% grayscale.
Scripts: train_xgb_boundary.py (shipped detector), extract_audio_features.py
(per-second log-PSD), downstream_presence.py (the A/B above), density_floor.py
(fallback for detection-starved films), plus the LSTM/DE explorations kept
for provenance. Model: models/scene_boundary_xgb.json.
Not yet wired into the live C++ pipeline — boundaries are a post-EOF step in
the sink (like flood-fill itself); libxgboost C++ integration is the next step.
docs/rep4-optimizer-results.md is the main deliverable: the model bake-off +
threshold re-tune experiment log, including the ROCm teardown deadlock root
cause and fix, DE concurrency tuning, the 16-combo results table, held-out
validation against 5 films never seen by the optimizer (macro F1 67.4% vs.
75.3% training — a real generalization gap), the frozen-bbox "ghost track"
failure mode found via annotated frame evidence, calibration curves per model,
and an isolated-effects breakdown of gallery scope vs. pose expansion.
MkDocs site (mkdocs.yml, docs/index.md) renders docs/*.md; scripts/docs/
pulls referenced images from the artifact registry and generates the
calibration chart at build time (see the tooling commit) rather than
committing images to the repo.
experiments/ now keeps only scripts + README + SESSION_STATE.md in git — every
data artifact (galleries, dumps, X-Ray corpus, montage frames, trajectories,
manifests, results) moved to the Gitea package registry. film-lut.template.json
is the committed placeholder for the gitignored file-lut.json (real local
movie paths, never shared — some source filenames carry scene-release tags).
Adds models/transnetv2.onnx (via Git LFS, matching the other ONNX models) for
the new scene-detection path.
dtourolle