ICCV Reproducibility

ICCV 2025 imposed no compute-reporting form and no reproducibility checklist that could be verified at check time (2026-07-08) — which means the venue's reproducibility bar is enforced socially: by reviewers who re-implement things for a living, and by a two-year horizon in which your paper is the standing reference until the next ICCV. Absent a form, the paper itself must carry the full disclosure. This skill is the audit.

The two-year checkability test

A CVPR paper gets superseded in twelve months; an ICCV paper's numbers get re-quoted, re-run, and re-attacked for at least twenty-four. Write every result so that a stranger in the next odd year can adjudicate a discrepancy:

• Dataset version and split files named (not "standard split" — the standard moves), with checksums where licenses allow.
• Metric implementation cited by repo and version; identically named metrics differ across codebases by more than typical paper deltas.
• Pretraining corpus and checkpoint identified for every initialization; a gain that rides an undisclosed web-scale pretrain is a different claim than the paper makes.
• Evaluation resolution, crop policy, and test-time augmentation stated per table, since these silently absorb whole points.

Foundation-model era: pin the protocol, not just the seed

Much post-2023 ICCV work evaluates around large pretrained models, which adds reproducibility failure modes that classical training-recipe disclosure never covered:

A "zero-shot" table whose prompt engineering is unstated is not zero-anything; reviewers at ICCV increasingly ask.

Recipe as a build artifact

Maintain one machine-readable record per reported row, from the first experiment, and generate the implementation section from it rather than reconstructing memories in deadline week:

# ledger/tab2_row5.toml — the row is reproducible iff this file is complete
model      = "ours-large"
init       = "vitl14-<hash>, corpus: <name+version>"
data       = { train = "co3d-v2@sha256:...", eval = "co3d-v2-test-list.txt" }
schedule   = { optim = "adamw", lr = 3e-4, epochs = 60, batch = 512, warmup = 5 }
aug        = ["rrc-336", "hflip"]
seeds      = [0, 1, 2]            # or [0] with flagged=true
hardware   = "16xA100-40G, bf16"
eval       = { resolution = 336, tta = false, metric_impl = "<repo>@<tag>" }
command    = "python train.py -c configs/tab2_row5.toml"

The ledger also answers rebuttal-week questions in minutes ("which schedule made Fig. 5?") — at ICCV those questions arrive in a seven-day window in May.

Variance honesty at vision budgets

Nobody multi-seeds a 16-GPU week ten times, and pretending otherwise persuades no one. The defensible pattern, stated in the paper's own words: cheap decisive experiments (the headline ablation, the small-backbone variant) run with ≥3 seeds and reported as mean ± std; the flagship run flagged explicitly as single; and no claim in the abstract resting on a margin smaller than the seed noise visible in your own tables. For stochastic evaluation (generation, sampling- based detection), repeat the evaluation pass and report its spread separately from training variance — the two get conflated constantly.

Compute disclosure without a form

No mandated form means you choose the disclosure, and the cheap honest version is one paragraph: total GPU-hours for the flagship, per-experiment cost for the grid, hardware and precision, and wall-clock per training run. Two reasons to volunteer it. Reviewers calibrate "simple and effective" claims against what the method costs to obtain; and any efficiency or "real-time" adjective in your abstract is unfalsifiable without named hardware — an easy weakness for a reviewer to poke in a cycle where you get one page of rebuttal to answer.

Withheld test sets and server etiquette

Benchmarks with evaluation servers turn your test number into a receipt rather than a rerunnable command. Record submission IDs and dates in the ledger, stay inside per-week submission budgets (tuning on the server is the field's canonical sin and organizers publish shame lists), and always give readers the validation-set protocol whose numbers predict the server's — that is what they will actually reproduce.

Determinism paragraph, written once

State the posture instead of implying perfection: which RNGs were seeded, whether deterministic kernels were enabled (and the throughput cost if not), known nondeterminism sources (scatter atomics, multi-GPU reduction order, dataloader scheduling), and the reproduction tolerance you measured across identical-seed reruns. One measured tolerance sentence ("±0.15 mIoU across nodes") converts future "failed to reproduce" issues into calibration checks.

Reverify each cycle

• Whether 2027 introduces any reproducibility checklist, compute form, or code-submission expectation (none verified for 2025).
• Benchmark version churn since the last cycle — two years is long in dataset time (iccv-experiments covers the drift audit).
• Current supplement constraints that bound how much recipe detail ships.

Output format

[Checkability grade] two-year test: pass / gaps
[Ledger coverage] rows with complete recipes: n/m
[Foundation-model pins] checkpoints · prompts · vocabularies · API versions: pinned?
[Variance] multi-seeded: <list>; flagged single runs: <list>; claims vs noise: OK?
[Compute paragraph] present with hardware + GPU-hours: yes/no
[Fix list] <ordered by what a re-implementer hits first>