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?
| Artifact is primarily... | Route to | Blinding | Judged on |
|---|---|---|---|
| A reusable software system/framework | Open Source Software Competition | Single-blind | Adoption, quality, license, docs |
| A new dataset/benchmark | Dataset track | Single-blind | Scale, quality, ethics, usefulness |
| A reproduction of published results | Reproducibility track | Single-blind | Whether results rebuild; ACM badges |
| Supporting evidence for a method paper | Main-track supplement | Double-blind | Whether it backs the paper's claims |
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.
textreview/ -> 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
textbefore 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
text[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>
