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Get Started Free →Use when deciding whether a project belongs at CVPR or should route elsewhere, covering what counts as a vision contribution at the field's flagship, fit tests for methods, datasets, and application papers, realistic odds at 25% acceptance and 16k submissions, and routing to ICCV, ECCV, WACV, 3DV, NeurIPS, or a journal.
.claude/skills/brycewang-stanford-cvpr-topic-selection/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-05 | ✗→✓ | ▲ Improved | 62% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 14% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 27% | 0% |
| case-13 | ✗→✓ | ▲ Improved | -19% | 0% |
| case-16 | ✗→✓ | ▲ Improved | 30% | 0% |
CVPR is the largest venue in computer vision and one of the largest in all of science — 16,092 reviewed submissions and 4,090 acceptances in 2026. Size cuts both ways: almost any vision-adjacent topic has a reviewer pool there, and almost any weakness has a reviewer who has seen it a hundred times. This skill decides whether to feed the machine before other skills decide how.
Strip the engineering and ask: is the contribution a claim about visual data or visual computation? CVPR's 2026 program clustered exactly there — the largest areas were image/video synthesis and generation; vision+language and reasoning; multimodal learning; 3D from multi-view and sensors; and medical/biological vision (official program announcement). Contributions where vision is merely the demo domain — a generic optimizer tested on ImageNet, an ML theory result with a CIFAR table — historically route better to NeurIPS/ICML, where the reviewer pool evaluates the actual claim.
| You have… | CVPR-shaped if… | Warning sign | |---|---|---| | A method/architecture | It solves a visual mechanism (geometry, temporal, pixels-to-structure), with benchmark wins + ablations | Gain vanishes under matched backbones | | A dataset/benchmark | It unlocks a task the field cannot currently study, with baselines and analysis | "Bigger than the last one" is the whole pitch (and release is due at camera-ready) | | A systems/efficiency result | Accuracy-per-FLOP frontier moves; CRF-style reporting is your friend | Speedup only on your hardware story | | A vision-language model result | The visual grounding is the contribution | It's an LLM paper wearing an image encoder | | An application (medical, agriculture, driving) | A general vision insight travels beyond the application | Domain novelty only → domain venue or WACV | | Theory about vision | Predicts something checkable in experiments | Pure theory → NeurIPS/ICML/SIGGRAPH-adjacent |
benchmarks reviewers will demand, with the compute you actually have?
the three nearest papers? (If not yet, see cvpr-related-work first.)
entangled trick?
or is it only a fourth-decimal metric story?
duties with desk-reject enforcement, a one-page January rebuttal — the process itself consumes a person-month.
textContribution core → First-choice venue ────────────────────────────────────────────────────────── Flagship vision method/benchmark → CVPR (Nov) — or ICCV/ECCV, same bar, different months: pick by readiness date Solid but not flagship-flashy; → WACV (applications-friendly CVF venue) applications emphasis 3D/geometry-centric community → 3DV (also CVF-affiliated), or CVPR 3D areas Learning theory / generic ML → NeurIPS / ICML / ICLR Graphics-adjacent synthesis → SIGGRAPH (different review culture entirely) Mature, extended, archival → TPAMI / IJCV (journal timelines, no rebuttal sprint, room beyond 8 pages) Early or niche idea → CVPR workshops (separate CFPs, lower stakes, same audience walking past your poster)
CVPR vs. ICCV/ECCV is rarely a quality question — the bar is comparable and reviewer pools overlap — it is a calendar question: which deadline does your evidence mature for? Submitting a month early to the "bigger name" with a missing ablation is how teams donate a cycle.
→ Not CVPR-shaped yet. The contribution is domain data + recipe. Routes: WACV (applications) or a domain venue — unless analysis reveals a general insight about when VLM grounding fails, which could anchor a CVPR paper with broader experiments.
consistent, +X on three benchmarks, 2ms overhead." → CVPR-shaped. Visual mechanism, plug-in generality, ablatable, cheap to evaluate broadly; the risk to audit is baseline freshness.
theorem." → Split decision. As stated, it is an ML-methods paper (NeurIPS/ICML reviewers evaluate the theorem properly). It becomes CVPR-shaped only if the loss exploits something visual (spatial structure, augmentation geometry) and the evidence spans vision tasks beyond classification.
25.42% acceptance means the modal outcome for a competent paper is rejection, and tier outcomes concentrate attention further (in 2026, ~3–4% of the program presented orally). Choose CVPR when the upside justifies that variance: maximal audience (about 12,200 registrants in 2026), industrial visibility, and the strongest possible signal when a benchmark claim survives this particular gauntlet.
The workshop program (separate CFPs, typically spring deadlines for a June conference) is a legitimate destination, not a consolation prize: new-task papers build their first community there, datasets get early adopters, and the audience walking past a workshop poster is the same 12,000-person crowd. Route to a workshop when the idea is promising but the main-conference evidence bar (leaderboard proximity, full ablations) is a cycle away — and note that workshop publication may interact with later dual-submission rules, so check both CFPs before using one as a stepping stone.
posts).
16k/25% figures above are the 2026 snapshot, not a constant.
text[Verdict] CVPR / sibling (which) / journal / workshop / not yet [Core claim] <one sentence, visual-contribution phrasing> [Fit evidence] leaderboard distance · nameable delta · ablatable · visual evidence [Process tax] team can cover duties + rebuttal week: yes/no [Route if not CVPR] <venue + verified deadline> [Ripeness gap] <what must exist before committing>
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-03 | pass→pass | 16,984 | 13,288 | -22% | 1 | 1 | 0% | 2,488 | 2,947 | +18% | 0 | 0 | — |
case-01 | fail→fail | 13,150 | 13,810 | +5% | 1 | 1 | 0% | 2,074 | 3,136 | +51% | 0 | 0 | — |
case-02 | pass→pass | 19,889 | 14,274 | -28% | 1 | 1 | 0% | 2,458 | 3,091 | +26% | 0 | 0 | — |
case-04 | pass→pass | 18,118 | 13,506 | -25% | 1 | 1 | 0% | 2,069 | 2,993 | +45% | 0 | 0 | — |
case-05 | fail→pass | 17,657 | 15,984 | -9% | 1 | 1 | 0% | 2,085 | 3,378 | +62% | 0 | 0 | — |
case-06 | pass→pass | 18,494 | 6,619 | -64% | 1 | 1 | 0% | 2,441 | 2,746 | +12% | 0 | 0 | — |
case-07 | pass→pass | 14,399 | 15,710 | +9% | 1 | 1 | 0% | 2,377 | 3,220 | +35% | 0 | 0 | — |
case-08 | pass→pass | 19,390 | 13,227 | -32% | 1 | 1 | 0% | 2,401 | 3,057 | +27% | 0 | 0 | — |
case-09 | pass→pass | 16,254 | 15,812 | -3% | 1 | 1 | 0% | 1,619 | 3,354 | +107% | 0 | 0 | — |
case-10 | fail→pass | 21,753 | 13,495 | -38% | 1 | 1 | 0% | 2,590 | 2,962 | +14% | 0 | 0 | — |
case-11 | pass→pass | 20,631 | 11,125 | -46% | 1 | 1 | 0% | 2,247 | 3,195 | +42% | 0 | 0 | — |
case-12 | fail→pass | 20,889 | 11,072 | -47% | 1 | 1 | 0% | 2,539 | 3,223 | +27% | 0 | 0 | — |
case-13 | fail→pass | 22,978 | 14,243 | -38% | 1 | 1 | 0% | 3,595 | 2,921 | -19% | 0 | 0 | — |
case-14 | fail→fail | 13,824 | 17,257 | +25% | 1 | 1 | 0% | 2,194 | 3,444 | +57% | 0 | 0 | — |
case-15 | pass→pass | 17,807 | 13,560 | -24% | 1 | 1 | 0% | 2,015 | 2,998 | +49% | 0 | 0 | — |
case-16 | fail→pass | 23,381 | 16,176 | -31% | 1 | 1 | 0% | 2,488 | 3,236 | +30% | 0 | 0 | — |
case-17 | pass→pass | 21,612 | 18,851 | -13% | 1 | 1 | 0% | 2,624 | 3,731 | +42% | 0 | 0 | — |
case-18 | fail→pass | 22,403 | 8,843 | -61% | 1 | 1 | 0% | 2,791 | 3,039 | +9% | 0 | 0 | — |
case-19 | fail→fail | 22,474 | 14,481 | -36% | 1 | 1 | 0% | 2,646 | 3,171 | +20% | 0 | 0 | — |
case-20 | pass→pass | 14,484 | 13,668 | -6% | 1 | 1 | 0% | 2,408 | 3,035 | +26% | 0 | 0 | — |
case-21 | pass→pass | 24,080 | 23,822 | -1% | 1 | 1 | 0% | 3,085 | 4,608 | +49% | 0 | 0 | — |
case-22 | pass→pass | 23,352 | 23,641 | +1% | 1 | 1 | 0% | 3,154 | 4,869 | +54% | 0 | 0 | — |
case-23 | pass→pass | 17,262 | 25,158 | +46% | 1 | 1 | 0% | 2,638 | 4,479 | +70% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +26 percentage points is the difference between those two pass rates over the 23 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.