acmmm-experiments
Use when designing or auditing the experiments of an ACM MM (ACM Multimedia) paper — matched baselines per modality, ablations that isolate the cross-modal fusion, user studies or QoE measurement where the claim is subjective, dataset and media licensing, and honest compute reporting, so evidence supports a multimedia claim.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acmmm-experiments --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# ACM MM Experiments Use this to make an ACM Multimedia paper's evidence match its claim. The reviewer's implicit questions are: does it work, **does the cross-modal part cause the gain**, when does it fail, and — if the target is perceptual — do people actually prefer it. ## The four questions and how to answer them | Question | Evidence that answers it | |---|---| | Does it work? | The headline metric on a recognized benchmark, against strong, matched baselines | | Does the *fusion* cause the gain? | A leave-one-modality-out / component ablation isolating the cross-modal term | | When does it fail? | Failure cases per modality (e.g., noisy audio, missing captions) shown honestly | | Do people prefer it? | A user study with reported N, protocol, and inter-rater agreement — for subjective claims | The second row is what separates an ACM MM experiment section from a single-modality one: if removing a modality does not move the result, the paper is not really cross-modal. ## Matched baselines - Compare against the **strongest** existing method, re-run under your data and preprocessing where feasible, not a weakened reimplementation. - Include a **late-fusion / naive-concatenation** b
- The four questions and how to answer them
- Matched baselines
- Ablations that isolate the cross-modal claim
- User studies and QoE
- Data, media, and compute honesty
- Benchmarks and metrics per modality
- Statistical reporting
- Common ACM MM experiment failures
- Output format
What does the acmmm-experiments skill do?
Use when designing or auditing the experiments of an ACM MM (ACM Multimedia) paper — matched baselines per modality, ablations that isolate the cross-modal fusion, user studies or QoE measurement where the claim is subjective, dataset and media licensing, and honest compute reporting, so evidence supports a multimedia claim.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acmmm-experiments --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.