본문으로 건너뛰기
cccskills
무료GitHub에 공개

acmmm-writing-style

Use when revising an ACM MM (ACM Multimedia) paper for house style — putting the cross-modal contribution on the first page, framing media (figures, video, audio) as evidence rather than decoration, making the fusion the visible claim, and compressing the argument into a 6-8 page ACM sigconf body with references-only overflow.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

실행 점수 미검증

설치 방법 보기

포함된 파일(1)

  • SKILL.md5.1 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

ACM MM Writing Style

Use this to revise an ACM Multimedia draft for the venue's expectations. The reader is a busy reviewer scanning across sixteen thematic areas; the paper must announce what is multimedia about it before the model diagram.

The ACM MM first-page arc

Lead the abstract and first paragraph with: problem → why one modality is insufficient → the cross-modal method or system → media-grounded evidence → why it matters for multimedia. The multimedia contribution belongs on page one; a paper that opens with a single-modality benchmark reads as a CVPR or ACL paper that wandered in.

  • State the fusion or systems mechanism as the contribution, not a backbone swap.
  • Make every media element do work: a teaser figure that shows the cross-modal signal, a video that is evidence for a claim, an audio clip that a reader can check.
  • Scope claims to what the evidence supports; "improves engagement" needs a measured engagement result behind it.

Media-as-evidence table

Media elementWeak (decoration)Strong (evidence)
Teaser figureA pretty system diagramThe moment where modalities disagree and the method wins
Qualitative gridCherry-picked successesPaired success/failure across modalities with captions that state the point
Supplementary video"See our results"A clip tied to a specific claim, with the baseline shown alongside
Audio sampleAn unlabeled waveformThe case the vision-only baseline misses, annotated

Compression into the sigconf body

The 6–8 page body is short by ACM standards, and figures compete with text for space.

  • Push proofs, full protocols, extra qualitative media, and hyperparameter tables to the supplement; keep the body's argument self-contained without them.
  • Write self-contained captions — a reviewer reading only figures and captions should grasp the cross-modal claim.
  • Budget page space before writing: decide which two or three media elements earn body space and which move to the supplement.

Revision passes

Pass 1 (contribution): Does page one name the cross-modal/systems contribution?
Pass 2 (fusion): Is the mechanism the claim, and is it ablated later?
Pass 3 (media): Does each figure/clip support a specific claim, with a self-contained caption?
Pass 4 (scope): Is every "better/more engaging/higher quality" tied to a measured result?
Pass 5 (budget): Does the body fit 6-8 sigconf pages with overflow holding references only?

Title and abstract for cross-area reviewers

Your reviewers may come from different thematic areas, so the title and abstract have to be legible to a vision person, an audio person, and a systems person at once.

  • Put the modalities and the mechanism in the title where natural ("audio-visual," "text-and- image," "cross-modal") so area chairs assign the right reviewers.
  • Make the abstract's first two sentences carry the whole contribution; a reviewer triaging many papers may read little more.
  • Avoid single-community jargon in the abstract; define the one term your cross-area readers will not share.

A body-budget worked pass

Treat the 6–8 page limit as a budget you allocate before writing prose:

p1  intro: contribution + why one modality fails + teaser figure
p2  related work (tight) + problem setup
p3-4 method: the fusion/alignment mechanism, one architecture figure
p5-6 experiments: main table, the decisive ablation, failure cases, user-study summary
p7-8 discussion + limitations; references spill onto the overflow pages (references only)

If a section will not fit, move detail to the supplement rather than shrinking the font or margins — template tampering is a desk-reject risk, and a cramped body reads worse than a clean one with a fuller supplement.

Common ACM MM style failures

  • Modality as garnish — audio/text mentioned but never shown to matter; fix by leading with the seam and ablating it.
  • Vision-paper voice — the whole framing is a benchmark race; fix by foregrounding the multimedia question.
  • Unbacked perceptual claims — "more natural," "more engaging" with no user study; fix by measuring or softening.
  • Caption starvation — figures that only make sense from the body text; fix by making captions stand alone.
  • Overflow abuse — method text pushed onto the references pages; those pages are for references only, and misuse risks desk reject.

Output format

[First-page verdict] multimedia contribution up front / buried
[Fusion visibility] mechanism is the claim / hidden behind a backbone
[Media evidence] each element earns its place / decorative elements: <list>
[Scope] claims matched to evidence / overclaims: <list>
[Page budget] fits 6-8 sigconf pages / over by <n>
[Top three fixes] <ordered>

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

brycewang-stanford/Awesome-Journal-Skills1.3천2026년 9월 27일 업데이트

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

brycewang-stanford/Awesome-Journal-Skills1.3천2026년 9월 27일 업데이트

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

brycewang-stanford/Awesome-Journal-Skills1.3천2026년 9월 27일 업데이트

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

brycewang-stanford/Awesome-Journal-Skills1.3천2026년 9월 27일 업데이트

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

brycewang-stanford/Awesome-Journal-Skills1.3천2026년 9월 27일 업데이트

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

brycewang-stanford/Awesome-Journal-Skills1.3천2026년 9월 27일 업데이트

brycewang-stanford의 스킬 모두 보기

이 스킬의 문제 신고