Use when building or auditing the related-work section of an ACM MM (ACM Multimedia) paper — covering the multimedia literature spread across vision, audio/speech, language, HCI/QoE, and systems, handling arXiv-speed concurrency, keeping citations double-blind, and verifying that cited "ACM MM papers" are ACM MM and not ICMR, MMSys, CVPR, or TOMM.
The English summary is being prepared. Showing the original description.
Run score Not tested
See how to installReview the instructions the agent will receive before you install.
Use this to position an ACM Multimedia paper against a literature that is unusually spread out: a cross-modal contribution touches several single-modality communities plus the multimedia venues that combine them.
A strong ACM MM related-work section shows command of all shelves the contribution touches, not just the author's home community:
| Task | What to do | Failure it prevents |
|---|---|---|
| Cover each modality you use | Cite the current best single-modality work per stream | "You ignored the vision literature" |
| Cite the fusion lineage | Trace the cross-modal line your method extends | "No delta over existing fusion" |
| Verify the venue string | Confirm each "ACM MM" cite on dblp conf/mm | Misattributing an ICMR/CVPR paper to ACM MM |
| Handle concurrency | Note contemporaneous arXiv work honestly | "You missed / overclaimed novelty vs. X" |
| Keep it blind | Cite your own prior work in third person | Double-blind violation |
Do not enumerate. For each closest neighbor, state in one clause what it did and in one clause what your paper adds — and make sure the delta is cross-modal, since "we swap a better encoder" is a single-modality delta a reviewer will discount.
[Neighbor] Late-fusion audio-visual highlight scoring (VenueYear).
[Their move] Average per-modality scores.
[Our delta] Score the timing DISAGREEMENT between streams — a signal averaging cannot represent.
Multimedia sub-areas move fast and preprint heavily. Acknowledge genuinely concurrent work (roughly same-window preprints) as concurrent rather than prior, do not claim to beat a method you did not run, and do not silently drop a close preprint — a reviewer who knows it reads the omission as evasion.
Pass 1: list the modalities and sub-fields your contribution touches (the shelves).
Pass 2: for each shelf, cite the current strongest 2-3 works you build on or beat.
Pass 3: trace the FUSION lineage — the cross-modal line your method extends — as its own thread.
Pass 4: add same-window arXiv work as concurrent, not prior.
Pass 5: verify every "ACM MM" cite on dblp; convert your self-cites to third person.
A reader should finish the section able to name which prior fusion approach you improve on and why the single-modality shelves are covered but not the whole story.
Because a cross-modal paper sits between communities, the dangerous omission is usually the other community's closest work — a vision-trained author who misses the audio or IR paper that already did half the job. Before submitting, ask a question from each shelf's perspective: "what would an audio reviewer, an IR reviewer, and a systems reviewer each say I missed?" That triage catches the omissions that sink cross-area papers.
Before submission, spot-check every citation that claims an ACM MM placement against dblp's
conf/mm edition record. The common traps: ICMR papers cited as ACM MM, TOMM journal
articles cited as the conference, and CVPR/ICCV vision papers cited as multimedia. Fix the
venue string or the claim that rests on it.
[Shelf coverage] vision/audio/language/HCI-QoE/multimedia — <covered / gaps>
[Fusion lineage] traced / missing
[Deltas] cross-modal and specific / single-modality or vague: <list>
[Concurrency] handled / risky omissions: <list>
[Blindness] clean / leaks: <list>
[Venue hygiene] verified / suspect cites: <list>
No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance artifact releases without violating double-blind or immutable-supplement rules.
The English summary is being prepared. Showing the original description.
Use when drafting an AAAI author response (rebuttal) under the single short character-limited author-feedback window, the no-URL rule, no-new-results guidance, AI-generated-review handling, and the AAAI two-phase review process where Phase-2 papers receive one feedback round before SPC/AC discussion.
The English summary is being prepared. Showing the original description.
Use when preparing an accepted AAAI paper for camera-ready source submission to AAAI Press, including proceedings page limits, two-column template compliance, copyright transfer, purchased extra technical pages, deanonymization, registration, oral or poster presentation, and final public artifact release.
The English summary is being prepared. Showing the original description.
Use when designing or auditing AAAI experiments for the broad-AI program committee, including baselines, ablations, statistical significance, robustness, human evaluation, AI-for-Social-Impact and alignment/safety evidence, compute and cost reporting, and reproducibility-checklist alignment for Phase-1 survival.
The English summary is being prepared. Showing the original description.
Use when positioning an AAAI paper's novelty against archival work, contemporaneous arXiv or workshop papers, and AAAI/IJCAI/NeurIPS/ICML/ICLR neighbors across the broad AI scope, while staying inside AAAI's dual-submission and AI-as-source policy constraints and writing a related-work section legible to non-specialist reviewers.
The English summary is being prepared. Showing the original description.
Use when strengthening an AAAI paper's reproducibility checklist (placed after references), experimental traceability, seed and hyperparameter reporting, compute and cost disclosure, dataset access and licensing, code/data ZIP readiness, and the claim-to-evidence map that Phase-1 reviewers use to judge rigor across AAAI's broad AI scope.
The English summary is being prepared. Showing the original description.