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Acmmm Related Work

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brycewang-stanford
acmmm-related-work

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.

Overview

Publisherbrycewang-stanford
RepositoryAwesome-Journal-Skills
Skill nameacmmm-related-work
Stars
1.2K
Forks
159
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by brycewang-stanford on GitHub. Read the source before you install it.

Installation

Install the Acmmm Related Work AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACM-MM-Skills/skills/acmmm-related-work
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git /tmp/Awesome-Journal-Skills
mkdir -p .claude/skills
cp -r /tmp/Awesome-Journal-Skills/ACM-MM-Skills/skills/acmmm-related-work .claude/skills/acmmm-related-work
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Acmmm Related Work in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Acmmm Related Work on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Acmmm Related Work is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

ACM MM Related Work

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.

The five multimedia shelves

A strong ACM MM related-work section shows command of all shelves the contribution touches, not just the author's home community:

  • Vision — the visual side (CVPR/ICCV/ECCV) your method builds on or competes with.
  • Audio/speech and music — the acoustic side (ICASSP, INTERSPEECH, ISMIR) if a stream is audio.
  • Language — the text side (ACL/EMNLP) if captions, transcripts, or descriptions matter.
  • HCI / QoE / human-centric — perception, engagement, and interaction (CHI, QoMEX) when the claim is subjective.
  • Multimedia proper — ACM MM, ICMR, MMSys, and TOMM, where these threads are combined.

Coverage vs. venue-hygiene table

TaskWhat to doFailure it prevents
Cover each modality you useCite the current best single-modality work per stream"You ignored the vision literature"
Cite the fusion lineageTrace the cross-modal line your method extends"No delta over existing fusion"
Verify the venue stringConfirm each "ACM MM" cite on dblp conf/mmMisattributing an ICMR/CVPR paper to ACM MM
Handle concurrencyNote contemporaneous arXiv work honestly"You missed / overclaimed novelty vs. X"
Keep it blindCite your own prior work in third personDouble-blind violation

Positioning, not listing

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.

text
[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.

Concurrency under arXiv speed

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.

Double-blind citation hygiene

  • Cite your own earlier papers in the third person ("Prior work [X] showed..."), never "our previous paper."
  • Avoid anonymity leaks through a dataset, system name, or repository that only your group uses.
  • The single-blind tracks (Reproducibility, Open Source Software, Dataset) relax this — but the main track and Brave New Ideas do not.

Building the section in passes

text
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.

The multimedia-specific omission risk

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.

Venue-verification pass

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.

Output format

text
[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>

Frequently asked questions

What does the Acmmm Related Work AI skill do?

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.

Why use Acmmm Related Work on TypingMind?

Because you install it once and use it with any model. Acmmm Related Work is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Acmmm Related Work in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACM-MM-Skills/skills/acmmm-related-work. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Acmmm Related Work?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Acmmm Related Work?

As many as you like. As long as a model supports skills, you can use Acmmm Related Work with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Acmmm Related Work AI skill free?

Yes. It is published on GitHub by brycewang-stanford under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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