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>
Source: brycewang-stanford/Awesome-Journal-Skills → ACM-MM-Skills/skills/acmmm-topic-selection/SKILL.md
1---2name: acmmm-topic-selection3description: 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.4---567# ACM MM Topic Selection89Use this before writing. ACM MM is strongest for work that **treats more than one medium at10once** — vision, audio/speech, language, sensor, interaction — or that advances the systems11that transport, index, and render media. The core test is whether the contribution *lives12at a seam between media*.1314## Fit test1516- Prefer ACM MM when the contribution is **cross-modal integration** (fusion, alignment,17 cross-modal retrieval/generation), a **media-systems** advance (streaming, QoE,18 transport), or a **human-centric media** result (emotion, aesthetics, engagement, art).19- Route to **CVPR/ICCV/ECCV** if the contribution is a pure computer-vision claim — a better20 detector, segmenter, or backbone with no essential second modality.21- Route to **ACL/EMNLP** if it is a pure language claim, and to **NeurIPS/ICLR** if it is a22 general ML method whose multimedia setting is incidental.23- Route to **ICMR** for retrieval-centric work that is more IR than multimedia systems, to24 **MMSys** for systems/networking-heavy media delivery, and to the **ACM TOMM** journal25 when the work needs journal-length treatment.26- Confirm the argument can be made convincing in a **6–8 page** sigconf body.2728## Fit signal table2930| Signal in the project | ACM MM reading |31|---|---|32| Two or more modalities that must interact for the result to hold | Core fit — the house genre |33| A reusable media system, framework, or dataset the community adopts | Core fit (Open Source / Dataset tracks) |34| Subjective quality / emotion / engagement measured with a user study | Core fit (human-centric areas) |35| A single-modality benchmark win (vision-only, text-only) | Better at CVPR/ICCV or ACL |36| Retrieval accuracy with no systems or cross-modal novelty | ICMR or SIGIR |37| Media delivery / networking with little content modeling | MMSys |3839## Picking the thematic area4041The main track is split into thematic areas (Multimodal Fusion; Generative and Foundation42Models; Search and Recommendation; Emotional and Social Signals; Art and Culture; Systems;43Transport and Delivery; Responsible Multimedia; and more). The area is not cosmetic — it44selects your reviewers. Name the **primary** area honestly; if the paper genuinely spans45two, pick the one whose reviewers can best judge the *contribution*, not the application.4647## Vignette: where an audio-visual model goes4849A project fuses lip motion and speech to improve transcription in noise. ACM MM reading:50strong fit — the gain exists *only* because two modalities correct each other, which is the51Multimodal-Fusion heartland. Strip the audio and keep a visual speech-recognition benchmark,52and the same project reads as a CVPR paper; strip the video and tune a language model on the53transcripts, and it becomes an ACL/speech paper. The multimedia contribution is the54correction *between* streams.5556## Routing within ACM MM5758Deciding it is an ACM MM paper is only half the choice; the track shapes everything after.5960| The project's center of gravity | Track within ACM MM |61|---|---|62| A method paper with cross-modal results | Main track (pick a thematic area) |63| A bold vision / new direction, evidence lighter | Brave New Ideas |64| A shared task entry with a competitive result | Multimedia Grand Challenge |65| A reusable, documented software system | Open Source Software Competition |66| A new dataset or benchmark | Dataset track |67| A rebuild of prior published results | Reproducibility track |6869The tracks differ in blinding and in what reviewers reward, so a strong-but-early idea does70better in Brave New Ideas than as a thin main-track method paper, and a great system does better71in the Open Source competition than buried as a main-track artifact.7273## The single-modality trap7475The most common misroute is a **single-modality paper wearing a multimedia costume**: audio or76text is bolted on but never shown to matter. If a leave-one-modality-out ablation would leave77the result essentially unchanged, the paper is not cross-modal, and an ACM MM reviewer will say78so. Either make the second modality load-bearing or route the paper to its true home venue79before writing — retrofitting multimedia framing onto a vision or NLP result rarely survives80review.8182## Sharpening moves before committing8384- Name the cross-modal or systems primitive: the fusion mechanism, the alignment objective,85 the delivery scheme, or the perceptual measure. If none exists, the ACM MM framing does86 not either.87- Decide the track early — main vs. Brave New Ideas (vision paper), Open Source Software,88 Dataset, or Reproducibility — because each has a different format and blinding rule.89- If the payoff is subjective, plan a **user study** now; ACM MM reviewers expect perceptual90 claims to be measured, not asserted.91- Thematic-area lists drift between cycles; scan the current Topics of Interest before final92 routing.9394## Output format9596```text97[Fit] strong ACM MM / possible ACM MM / better elsewhere98[Best venue] ACM MM / CVPR / ICCV / ACL / ICMR / MMSys / NeurIPS / TOMM / other99[Thematic area] <primary area, if ACM MM>100[Cross-modal or systems core] <one sentence>101[Top rejection risk] <single-modality framing / weak fusion / no user study / scope>102[Next action] <method, user study, framing, or venue switch>103```104105---106107**Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `ACM-MM-Skills/skills/acmmm-topic-selection/SKILL.md`
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