技能 人工智能 ACM多媒体论文写作风格指南

ACM多媒体论文写作风格指南

v20260724
acmmm-writing-style
这是一份针对ACM多媒体(ACM MM)论文的撰写和修改指南。它指导作者如何将论文的焦点从单一模态的性能指标,转移到跨模态的融合机制上。核心要求是让所有媒体元素(图、音、视频)充当直接证据,并在严格的6-8页篇幅内,将多媒体贡献清晰地展示在论文的第一页。
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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 element Weak (decoration) Strong (evidence)
Teaser figure A pretty system diagram The moment where modalities disagree and the method wins
Qualitative grid Cherry-picked successes Paired 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 sample An unlabeled waveform The 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>
信息
Category 人工智能
Name acmmm-writing-style
版本 v20260724
大小 5.13KB
更新时间 2026-07-28
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