> **Archived (July 2026).** This report covers the pre-opencv5 framework and the 4-model ArcFace/LVFace bake-off. It is superseded by the current [experiment log](model-bakeoff.md) for the opencv5 build. Kept for provenance; the numbers here are historical.

# Pose expansion: does promoting new poses mid-film help?

`expand_gallery`
([`src/gallery/track_gallery.hpp`](https://REPOLINK/src/gallery/track_gallery.hpp))
promotes a confidently identified track's novel-pose reference views into a
per-film, in-memory gallery annex. The idea: once the pipeline is confident
about an identity, a pose it has not seen before (turned head, different
lighting) becomes an extra reference for recognizing that actor again later
in the same film, without touching the baked gallery.

## Training-set signal

Averaged across the 3 compared models (r50 excluded), on the 4 films used
for optimization. These are the corrected, full-coverage figures, see the
[dropped-film note](model-bakeoff-2026-07.md#a-scoring-bug-worth-recording-dropped-film-evaluations)
in the experiment log for why an earlier version of this table overstated the
full-mode misID jump (209 → 864) that was itself partly a truncation artifact:

| scope | expansion | F1 | R | misID |
|---|---|---|---|---|
| full | off | 70.0% | 57.6% | 407 |
| full | on | 72.1% | 61.5% | 714 |
| restricted | off | 75.1% | 63.9% | 179 |
| restricted | on | 76.7% | 67.2% | 120 |

In restricted mode, expansion looks like a clean win: +1.6pp F1, +3.3pp
recall, lower misID. In full mode it looks like a recall-for-misID trade:
+2.1pp F1, +3.9pp recall, but misID rises from 407 to 714. See
[the full experiment log](model-bakeoff-2026-07.md) for the per-model breakdown.
This asymmetry motivated the question below: does turning expansion on
change what gets recognized frame by frame, or is the aggregate F1 shift
coming from something else.

## Held-out test

Same model, same tuned config, `expand_gallery` toggled on vs. off, nothing
else changed, full gallery mode, per-second scoring against X-Ray. This
isolates expansion from every other variable that differs between the
training-set rows above.

LVFace-B Glint360K, all 5 held-out films:

| film | F1 (exp) | F1 (noexp) | TPI delta | FN delta |
|---|---|---|---|---|
| Benny & Joon | 83.0% | 83.0% | -2 | +2 |
| Downton Abbey: A New Era | 56.1% | 56.2% | -7 | +7 |
| Lovelace | 77.5% | 77.4% | +33 | -33 |
| The Many Saints of Newark | 46.3% | 46.3% | +2 | -2 |
| Valerian and the City of a Thousand Planets | 74.1% | 74.1% | +2 | -2 |

ArcFace R18, Benny & Joon, r18's own tuned config: F1 77.1% for both, TPI
and FN identical, FPI differs by 2.

Every film, both models tested: F1 differs by 0.1-0.2pp, TPI/FN swings are
in the tens out of tens of thousands. This is noise, not a signal.
Expansion made no measurable difference to per-second on-screen
identification on any held-out film tested.

## Two methodology bugs caught during this check

Getting to the table above required catching two wrong turns, both worth
recording because they are exactly the kind of error that produces a false
positive "expansion helped" finding.

1. **Timeout truncation.** The first Downton Abbey `exp` replay was cut off
   by a 60-second subprocess timeout at about 76% through the film (5589 of
   7368 expected seconds). This silent data loss produced a large,
   convincing-looking TPI gap (47938 vs 52032) purely because one run was
   missing a quarter of the film. Caught by comparing `n_seconds` between
   runs before trusting any score delta; fixed by re-running with a longer
   timeout.
2. **Bbox-matching bug.** An early per-second raw-annotation diff matched
   each `exp` detection to the first `noexp` detection with IoU above 0.5,
   not the best-overlapping one. With 3 faces close together in frame, this
   produced spurious disagreements (for example "exp says Aidan Quinn,
   noexp says Johnny Depp" at the same second) that vanished once the match
   used the best-IoU candidate instead of the first one. Both configs had
   actually output the same three names at the same three boxes.

Both bugs independently pointed toward "expansion is doing something," and
both were artifacts of the comparison harness, not the pipeline. Before
trusting a dramatic before/after diff, check that both runs cover the same
seconds and that entities are matched by best overlap, not first found.

## Conclusion

The training-set aggregate effect, particularly the full-mode misID
increase, does not reproduce on held-out data. At minimum it
is far smaller than the training-set numbers suggested; it may be sampling
variation from only 4 training films rather than a generalizable
mechanism. Note the same *class* of harness bug appears twice in this
investigation, the timeout truncation in bug #1 above, and the dropped-film
aggregation that inflated the raw training-set misID figures. Both make an
inert config look consequential; both are reasons to distrust a dramatic
training-set delta until it survives on held-out films, which this one did
not. This does not mean `expand_gallery` never does anything: the
mechanism is real, and
[`track_gallery.hpp`](https://REPOLINK/src/gallery/track_gallery.hpp)'s
promotion logging confirms tracks get confirmed and views get promoted
into the annex on every film tested. It means whatever effect expansion
has on final per-second identification was too small to detect against 5
held-out films with this scoring method. A cleaner test would need either
more held-out films or a metric that can see the annex's direct
contribution, such as tagging which reference embedding won each match;
neither was in scope for this pass.

Do not treat the training-set exp/noexp numbers in
[the full experiment log](model-bakeoff-2026-07.md) as proof that expansion changes
real-world behavior in either direction. On the evidence gathered so far,
it does not move the needle enough to see.
