aaai-artifact-evaluati…
Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance…
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
Claude Code
$ npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill acmmm-related-work --agent claude-code
Codex
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acmmm-related-work -a codex -y
Cursor
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acmmm-related-work -a cursor -y
Gemini CLI
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acmmm-related-work -a gemini-cli -y
GitHub Copilot
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acmmm-related-work -a github-copilot -y
OpenCode
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acmmm-related-work -a opencode -y
Windsurf
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acmmm-related-work -a windsurf -y
name: acmmm-related-work description: 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.
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:
audio.
the claim is subjective.
| 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.
previous paper."
uses.
the main track and Brave New Ideas do not.
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>
Stanford REAP × CoPaper.AI · 由斯坦福实证方法论团队精选与维护 访问 copaper.ai 微信:CoPaper.AI 按 11 个主流学科板块覆盖 经管与商科 社会科学 人文学科 数学与物理科学 生命科学 医学与健康 工程与技术 计算机科学与 AI 体育科学 点击任一学科名可跳转到对应说明;每类下的代表子领域在正文总览中完整列出。下方封面墙按 venue 导航,完整分类见覆盖一览。 🧭 布局指南 · 📚 Skill Pack 一览 · ⚡ 如何使用 · 🧪 自动实证
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