acmmm-topic-selection

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>