/acmmm-topic-selection

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>