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cvpr-experiments

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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.

CVPR-Skills/skills/cvpr-experiments/SKILL.md

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# 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*
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