Wacv experiments

WACV Experiments

Use this to build evidence a WACV reviewer accepts in Round 1 instead of sending to Revise and Resubmit. The controlling idea: WACV reviews under two tracks, and each track has a different evidence bar. Facts are the WACV 2026/2027 cycles as read on 2026-07-09.

Evidence by track

Question the reviewer asks	Applications track	Algorithms track
Does it work where it must?	Metric under the deployment constraint (power/latency/light/data budget)	Metric on the standard benchmark
Is the comparison fair?	Baselines re-tuned to the same constraint, not their defaults	Baselines under matched backbone/compute
What does it cost?	Measured latency, wattage, memory on the named device	FLOPs/params/throughput reported honestly
When does it fail?	Failure cases under the real condition	Ablation isolating the mechanism
Is the gain real?	Uncertainty over sessions and seeds	Uncertainty over seeds; significance where small

The single most common WACV revision request is "you compared against baselines at their defaults, not under your constraint." Pre-empt it: an Applications claim is only supported if the baselines were given the same power/data/latency budget you operated under.

The four implicit questions

Design the experiment section to answer, in order: does it work, why, when does it fail, and what does it cost. For an applications paper the "what does it cost" row is load-bearing — a system that hits accuracy but blows the power budget has not made the contribution it claims.

Comparative assessment under the deployed condition

For each baseline B and each headline claim C under constraint K:
  1. Re-tune / re-run B under K (same budget, same data regime) — not B's paper defaults.
  2. Report your method and B on the same axis (e.g., boundary-F1 @ K watts).
  3. Include an unconstrained upper-bound reference so the reader sees the cost of K.
  4. Report each number with a spread (std over seeds and, for field/systems work, sessions).
If any baseline was run only at its default and not under K, the comparison is not yet fair.

Uncertainty and honesty

• Report variance: standard deviations over seeds, and for deployed/field systems over repeated sessions or devices, not a single run.
• Do not overclaim when a gap is within the spread; say "within a small margin" and show the numbers. WACV reviewers, being applications-minded, distrust hero runs.
• Keep the exact split, device, meter, and hyperparameters in the supplement so a Round 2 reviewer can confirm nothing changed silently between rounds.

Ablations that isolate the claim

An ablation should remove exactly the component your contribution rests on and show the metric move under the same condition. For an Algorithms-track paper this is the core of novelty; for an Applications-track paper, ablate the part that makes the system meet the constraint (the low-power front end, the data-efficient step), not a generic layer.

Reverify each cycle

• The two-track review criteria and any track-specific evidence expectations.
• Whether specific reporting (compute, energy) is requested in the current guidelines.
• Dataset licensing/anonymity rules for any data released with the paper.

Output format

[Track] Applications / Algorithms
[Works] headline metric under the required condition: <value ± spread>
[Fair] baselines run under the same constraint: yes/no
[Cost] latency/power/FLOPs on named device: <reported?>
[Fails] failure analysis under the real condition: <present?>
[Uncertainty] spread over seeds/sessions: yes/no
[R&R risk] <the comparison a reviewer would call unfair>