ACM MM Topic Selection

SkillDev tools

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.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the ACM MM Topic Selection skill

What this skill tells your AI

The instructions your AI receives, as published by brycewang-stanford/awesome-journal-skills in ACM-MM-Skills/skills/acmmm-topic-selection/SKILL.md and read by ahel’s review.

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>

Signals

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github.com/brycewang-stanford/awesome-journal-skills