version: "1.0.0" name: acmmm-artifact-evaluation description: Use when packaging code, models, datasets, or media as ACM MM (ACM Multimedia) artifacts — building the anonymous review package versus the public release, and choosing between the Open Source Software Competition, the Dataset track, the Reproducibility track, and main-track supplementary evidence, each with its own blinding and expectations.

ACM MM Artifact Evaluation

Use this to turn an ACM Multimedia project's code, models, media, and data into the right artifact for the right track. ACM MM has a track economy around artifacts, and the choice determines blinding, format, and what reviewers judge.

Which track is the artifact?

The named single-blind tracks exist because the artifact's identity cannot be hidden; a main-track method paper's artifact, by contrast, must be anonymous through review.

Two artifacts, two audiences

Plan both from the start:

• Anonymous review artifact — what reviewers see during double-blind review: an

anonymized repository, an anonymous data mirror, stripped media metadata, and a README that reveals no author identity.

• Public release artifact — what ships at/after camera-ready: the de-anonymized

repository, a permanent archive (DOI), the license, and the final dataset/model.

review/    -> anonymous repo, anon data mirror, no names in code/media, run instructions
release/   -> public repo + DOI, LICENSE, model weights, dataset card, citation

Open Source Software Competition

• The bar is a system others will use: clear install, documentation, examples, an

OSI-approved license, and evidence of quality or adoption.

• Reference models and reproducible examples matter more than a single benchmark number —

this is the lane exemplified by community frameworks and portable libraries.

Dataset track

• Ship a dataset card: collection method, size, splits, license, consent, and known

biases or limitations.

• Address ethics and rights explicitly, especially for user-generated or scraped media; a

dataset a reviewer cannot legally use is not a contribution.

Licensing and rights decisions

• Choose a code license (permissive vs. copyleft) and a data license separately; they are

not the same choice.

• For media, confirm you have the right to redistribute; where you cannot, provide a

retrieval script or agreement path instead of the raw files.

• Record third-party asset licenses so the release is clean.

Ethics and consent for media artifacts

Multimedia artifacts carry people's faces, voices, and content, so the ethics review is not a formality:

• Consent and rights — confirm you may redistribute the media; user-generated content often

cannot be re-hosted, so ship a retrieval script or agreement path instead.

• Privacy — remove or justify identifiable individuals who did not consent; a dataset of

scraped faces is a rejection risk regardless of its scale.

• Documentation — a dataset card that states collection method, consent, license, and known

biases is part of the contribution, not paperwork.

Timeline: review artifact, then release

before paper deadline:  anonymous review artifact ready (repo + data mirror, no identity)
during review:          reviewers/AC access the anonymous artifact
on acceptance:          build the public release (de-anonymized repo + DOI + license)
by camera-ready:        release replaces the anonymous mirror; dataset/model final

Plan the public release early even though it ships late — a scramble at camera-ready is how projects end up with a broken anonymous link and no working public archive.

Output format

[Track] Open Source / Dataset / Reproducibility / main-track supplement
[Blinding] correct for track / mismatch
[Review artifact] anonymous + runnable / gaps: <list>
[Release artifact] archived + licensed / gaps: <list>
[Rights] code+data+media licenses set / open questions: <list>
[Top fixes] <ordered>