cvpr-experiments
Use when designing or auditing the experimental program of a CVPR paper, covering benchmark and baseline selection under matched-compute fairness, the ablation study reviewers treat as mandatory, qualitative and failure-case evidence, efficiency metrics tied to the Compute Reporting Form, and generalization tests beyond a single dataset.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cvpr-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.
# CVPR Experiments CVPR runs on benchmark evidence: reviewers at the 2026 edition sorted 16,092 submissions largely by asking "do the tables prove the sentence?" This skill designs an experimental program that answers the four questions every vision review implicitly asks — does it work, why does it work, when does it fail, and what does it cost. ## Does it work: main comparisons - **Benchmarks**: use the datasets your subfield's last two cycles used, current versions, standard splits. A new task may justify a new benchmark, but then the benchmark itself becomes a contribution needing validation (and, if claimed as one, public release by camera-ready — verified 2026 policy). - **Baselines**: the leaderboard's current top methods plus the strongest *simple* baseline. The comparison that kills papers in review is the one you omitted because it was too strong. - **Fairness**: match backbones, pretraining corpora, input resolution, and training schedule wherever possible — or tabulate the mismatch explicitly. Beating a ResNet-era method with a ViT-L and calling it method innovation is the single most common CVPR review objection. - **Provenance**: mark which numbers are re-runs under y
- Does it work: main comparisons
- Why it works: the ablation is not optional
- When it fails: qualitative evidence with integrity
- What it costs: efficiency as a first-class axis
- Generalization: the second-dataset rule
- Budgeting the program before running it
- Statistical care, vision edition
- Reverify each cycle
- Output format
What does the cvpr-experiments skill do?
Use when designing or auditing the experimental program of a CVPR paper, covering benchmark and baseline selection under matched-compute fairness, the ablation study reviewers treat as mandatory, qualitative and failure-case evidence, efficiency metrics tied to the Compute Reporting Form, and generalization tests beyond a single dataset.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cvpr-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.