Skip to content
cccskills
FreePublished on GitHub

acmmm-topic-selection

Use when deciding whether a project is a genuine ACM MM (ACM Multimedia) contribution rather than single-modality work, choosing a thematic area, and routing between ACM MM, CVPR/ICCV, ACL/EMNLP, ICMR, MMSys, NeurIPS/ICLR, and the ACM TOMM journal by finding the cross-modal or media-systems core of the contribution.

The English summary is being prepared. Showing the original description.

See how to install

Files included (1)

  • SKILL.md5.5 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

ACM MM Topic Selection

Use this before writing. ACM MM is strongest for work that treats more than one medium at once — vision, audio/speech, language, sensor, interaction — or that advances the systems that transport, index, and render media. The core test is whether the contribution lives at a seam between media.

Fit test

  • Prefer ACM MM when the contribution is cross-modal integration (fusion, alignment, cross-modal retrieval/generation), a media-systems advance (streaming, QoE, transport), or a human-centric media result (emotion, aesthetics, engagement, art).
  • Route to CVPR/ICCV/ECCV if the contribution is a pure computer-vision claim — a better detector, segmenter, or backbone with no essential second modality.
  • Route to ACL/EMNLP if it is a pure language claim, and to NeurIPS/ICLR if it is a general ML method whose multimedia setting is incidental.
  • Route to ICMR for retrieval-centric work that is more IR than multimedia systems, to MMSys for systems/networking-heavy media delivery, and to the ACM TOMM journal when the work needs journal-length treatment.
  • Confirm the argument can be made convincing in a 6–8 page sigconf body.

Fit signal table

Signal in the projectACM MM reading
Two or more modalities that must interact for the result to holdCore fit — the house genre
A reusable media system, framework, or dataset the community adoptsCore fit (Open Source / Dataset tracks)
Subjective quality / emotion / engagement measured with a user studyCore fit (human-centric areas)
A single-modality benchmark win (vision-only, text-only)Better at CVPR/ICCV or ACL
Retrieval accuracy with no systems or cross-modal noveltyICMR or SIGIR
Media delivery / networking with little content modelingMMSys

Picking the thematic area

The main track is split into thematic areas (Multimodal Fusion; Generative and Foundation Models; Search and Recommendation; Emotional and Social Signals; Art and Culture; Systems; Transport and Delivery; Responsible Multimedia; and more). The area is not cosmetic — it selects your reviewers. Name the primary area honestly; if the paper genuinely spans two, pick the one whose reviewers can best judge the contribution, not the application.

Vignette: where an audio-visual model goes

A project fuses lip motion and speech to improve transcription in noise. ACM MM reading: strong fit — the gain exists only because two modalities correct each other, which is the Multimodal-Fusion heartland. Strip the audio and keep a visual speech-recognition benchmark, and the same project reads as a CVPR paper; strip the video and tune a language model on the transcripts, and it becomes an ACL/speech paper. The multimedia contribution is the correction between streams.

Routing within ACM MM

Deciding it is an ACM MM paper is only half the choice; the track shapes everything after.

The project's center of gravityTrack within ACM MM
A method paper with cross-modal resultsMain track (pick a thematic area)
A bold vision / new direction, evidence lighterBrave New Ideas
A shared task entry with a competitive resultMultimedia Grand Challenge
A reusable, documented software systemOpen Source Software Competition
A new dataset or benchmarkDataset track
A rebuild of prior published resultsReproducibility track

The tracks differ in blinding and in what reviewers reward, so a strong-but-early idea does better in Brave New Ideas than as a thin main-track method paper, and a great system does better in the Open Source competition than buried as a main-track artifact.

The single-modality trap

The most common misroute is a single-modality paper wearing a multimedia costume: audio or text is bolted on but never shown to matter. If a leave-one-modality-out ablation would leave the result essentially unchanged, the paper is not cross-modal, and an ACM MM reviewer will say so. Either make the second modality load-bearing or route the paper to its true home venue before writing — retrofitting multimedia framing onto a vision or NLP result rarely survives review.

Sharpening moves before committing

  • Name the cross-modal or systems primitive: the fusion mechanism, the alignment objective, the delivery scheme, or the perceptual measure. If none exists, the ACM MM framing does not either.
  • Decide the track early — main vs. Brave New Ideas (vision paper), Open Source Software, Dataset, or Reproducibility — because each has a different format and blinding rule.
  • If the payoff is subjective, plan a user study now; ACM MM reviewers expect perceptual claims to be measured, not asserted.
  • Thematic-area lists drift between cycles; scan the current Topics of Interest before final routing.

Output format

[Fit] strong ACM MM / possible ACM MM / better elsewhere
[Best venue] ACM MM / CVPR / ICCV / ACL / ICMR / MMSys / NeurIPS / TOMM / other
[Thematic area] <primary area, if ACM MM>
[Cross-modal or systems core] <one sentence>
[Top rejection risk] <single-modality framing / weak fusion / no user study / scope>
[Next action] <method, user study, framing, or venue switch>

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance artifact releases without violating double-blind or immutable-supplement rules.

The English summary is being prepared. Showing the original description.

brycewang-stanford/Awesome-Journal-Skills1.3KUpdated Sep 27, 2026

Use when drafting an AAAI author response (rebuttal) under the single short character-limited author-feedback window, the no-URL rule, no-new-results guidance, AI-generated-review handling, and the AAAI two-phase review process where Phase-2 papers receive one feedback round before SPC/AC discussion.

The English summary is being prepared. Showing the original description.

brycewang-stanford/Awesome-Journal-Skills1.3KUpdated Sep 27, 2026

Use when preparing an accepted AAAI paper for camera-ready source submission to AAAI Press, including proceedings page limits, two-column template compliance, copyright transfer, purchased extra technical pages, deanonymization, registration, oral or poster presentation, and final public artifact release.

The English summary is being prepared. Showing the original description.

brycewang-stanford/Awesome-Journal-Skills1.3KUpdated Sep 27, 2026

Use when designing or auditing AAAI experiments for the broad-AI program committee, including baselines, ablations, statistical significance, robustness, human evaluation, AI-for-Social-Impact and alignment/safety evidence, compute and cost reporting, and reproducibility-checklist alignment for Phase-1 survival.

The English summary is being prepared. Showing the original description.

brycewang-stanford/Awesome-Journal-Skills1.3KUpdated Sep 27, 2026

Use when positioning an AAAI paper's novelty against archival work, contemporaneous arXiv or workshop papers, and AAAI/IJCAI/NeurIPS/ICML/ICLR neighbors across the broad AI scope, while staying inside AAAI's dual-submission and AI-as-source policy constraints and writing a related-work section legible to non-specialist reviewers.

The English summary is being prepared. Showing the original description.

brycewang-stanford/Awesome-Journal-Skills1.3KUpdated Sep 27, 2026

Use when strengthening an AAAI paper's reproducibility checklist (placed after references), experimental traceability, seed and hyperparameter reporting, compute and cost disclosure, dataset access and licensing, code/data ZIP readiness, and the claim-to-evidence map that Phase-1 reviewers use to judge rigor across AAAI's broad AI scope.

The English summary is being prepared. Showing the original description.

brycewang-stanford/Awesome-Journal-Skills1.3KUpdated Sep 27, 2026

See all skills by brycewang-stanford

Report a problem with this skill