--- name: wacv-experiments description: Use when designing or auditing WACV experiments, covering Applications-track systems evidence (latency, power, robustness under real constraints) versus Algorithms-track matched-baseline novelty, comparative assessment under the deployed condition, uncertainty over seeds and sessions, ablations, and evidence that survives the two-round review. --- # 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 ```text 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 ```text [Track] Applications / Algorithms [Works] headline metric under the required condition: [Fair] baselines run under the same constraint: yes/no [Cost] latency/power/FLOPs on named device: [Fails] failure analysis under the real condition: [Uncertainty] spread over seeds/sessions: yes/no [R&R risk] ```