acmmm-topic-selection
GitHub辅助判断项目是否适合ACM MM会议，识别跨模态或媒体系统核心贡献。指导在CVPR、ACL、ICMR等会议及TOMM期刊间进行路由选择，并协助确定合适的主题领域。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acmmm-topic-selection -g -y
SKILL.md
Frontmatter
{
"name": "acmmm-topic-selection",
"description": "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."
}
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 project | ACM MM reading |
|---|---|
| Two or more modalities that must interact for the result to hold | Core fit — the house genre |
| A reusable media system, framework, or dataset the community adopts | Core fit (Open Source / Dataset tracks) |
| Subjective quality / emotion / engagement measured with a user study | Core 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 novelty | ICMR or SIGIR |
| Media delivery / networking with little content modeling | MMSys |
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 gravity | Track within ACM MM |
|---|---|
| A method paper with cross-modal results | Main track (pick a thematic area) |
| A bold vision / new direction, evidence lighter | Brave New Ideas |
| A shared task entry with a competitive result | Multimedia Grand Challenge |
| A reusable, documented software system | Open Source Software Competition |
| A new dataset or benchmark | Dataset track |
| A rebuild of prior published results | Reproducibility 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>
Version History
- 9f86f09 Current 2026-07-19 14:17
