How to Write a WACV Rebuttal: A Practical Guide for First-Time Submitters

A First-Timer's Real Dilemma

Recently, a first-time submitter posted a plea for help on an academic Reddit community. His paper submitted to WACV (Winter Conference on Applications of Computer Vision) had received scores of 5, 4, and 4, with reviewer confidence levels of 4, 3, and 4 respectively.

Those scores put the paper squarely on the borderline — not a clear accept, not an outright reject, but the classic situation where a strong rebuttal is your best shot at tipping the scales. What made things trickier was a set of problems that trip up many newcomers: the Meta Reviewer's feedback pointed in a different direction than the three regular reviewers', some of the reviews looked suspiciously AI-generated, and several requests seemed nearly impossible to address within the rebuttal's tight word limit.

This article uses that case as a starting point to walk through a systematic strategy for handling academic conference rebuttals — aimed at helping first-time submitters think clearly under pressure.

Reading the Signals Behind Review Scores

Scores and Confidence Together

In the review systems used by top CV/AI venues, you need to read scores and confidence levels together. A 5/4/4 combination lands somewhere between "weak accept" and "borderline reject" — typically a 5 means "leaning accept" and a 4 means "borderline negative."

Confidence is where it gets interesting. With confidence levels of 4, 3, and 4, the reviewers are fairly sure of their own assessments. High-confidence negative reviews are harder to flip through rebuttal because those reviewers believe they understood the paper well. The reviewer with a confidence of 3, on the other hand, is often your best target — they're essentially admitting some uncertainty in their own judgment.

Prioritizing Your Rebuttal Responses

When facing feedback from multiple directions, spreading your effort evenly is the worst thing you can do. The rational approach:

• Focus first on low-confidence reviewers whose concerns can be resolved through clarification (in this case, the one with confidence 3);
• Prioritize the Meta Reviewer's comments, since they hold the most sway over the final acceptance decision;
• For high-confidence reviewers with firm negative stances, weigh carefully whether the limited rebuttal space is worth spending there.

Handling Conflicts Between the Meta Reviewer and Regular Reviewers

This is the most analytically rich part of the case. The Meta Reviewer raised only two concerns, both relatively straightforward to address. The three regular reviewers, however, flagged a set of resubmission-level issues that pointed in a largely different direction.

Anchor to the Meta Reviewer's Judgment

In most conference workflows, the Meta Reviewer (also called the Area Chair, or AC) is the pivotal figure who synthesizes all reviews and makes the acceptance recommendation. When the Meta and the reviewers conflict, the Meta's opinion generally carries more decision-making weight.

Strategically, that means:

1. Address the Meta Reviewer's two points thoroughly and first, making it clear at the top of your rebuttal that these issues have been handled. This signals to the decision-maker that the paper's core concerns are manageable.
2. When responding to reviewer comments that diverge from the Meta's direction, you can subtly invoke the Meta's framing — for example, "As the Meta Reviewer identified regarding the core contribution..." — to use that authority to balance out reviewer skepticism.

Avoid Getting Pulled into Reviewer Rabbit Holes

Some reviewers will ask for extensive additional experiments or content that goes beyond the paper's scope. If those requests don't align with the Meta's concerns and drift away from the paper's main thread, you don't have to follow them down that path. Politely noting that the issue falls outside the current work's scope and could be addressed as future work is often wiser than forcing in hastily assembled data.

Responding to Potentially AI-Generated Reviews

The author mentioned that some reviews "looked like they were AI-generated." This is an increasingly common phenomenon in academia, and it introduces new challenges.

Stay Professional and Address the Substance

Even if you suspect a review was AI-generated, explicitly accusing it of being AI-written in your rebuttal is generally a bad move — you have no proof, and it risks irritating the Meta Reviewer or creating unnecessary friction.

The safer approach:

• Identify and clarify specific points that are vague, generic, or factually wrong. AI-generated reviews tend to be surface-level and disconnected from the actual paper content. You can point this out tactfully: "This concern may stem from a misunderstanding — Section X of the paper explicitly addresses..."
• If a review contains clear factual errors (e.g., claiming the paper lacks an experiment that is actually present), point that out politely but firmly with a specific reference. These kinds of clarifications are highly persuasive to the Meta Reviewer.

Use the Confidential Comment Channel

Some conferences allow authors to submit confidential comments to the AC. If you have strong grounds to believe a review is exceptionally poor quality or clearly irresponsible, you can calmly state the facts through this channel and let the AC decide — rather than escalating publicly in the rebuttal itself.

Rebuttal Structure and Practical Tips

The Classic Point-by-Point Structure

A solid rebuttal structure looks like this:

• Opening: Two or three sentences summarizing the paper's core contributions and highlighting the points reviewers agreed on;
• Body: Address each reviewer in turn (R1, R2, R3), using a "[summary of concern] → [response]" format for each point;
• For concerns shared by multiple reviewers, group them into a single dedicated section to avoid repetition.

Let Data and Experimental Results Do the Talking

Rebuttal space is tight — abstract arguments don't move the needle. If you can run a key experiment, ablation, or comparison in the time available, that will almost always be more convincing than a long written defense. Even a single new results table can meaningfully shift a reviewer's assessment.

Be Honest About What You Can't Fix Right Now

For requests that are genuinely "too hard to resolve in a rebuttal," it's better to be upfront than to dodge or bluff:

• Acknowledge the validity of the concern and explain that it will be addressed in the camera-ready version or follow-up work;
• Explain why, within your current framework, the issue doesn't undermine the core conclusions;
• Provide partial evidence that shows the direction is feasible.

Reviewers and ACs value genuine intellectual honesty and sound research judgment far more than a perfect-sounding answer to every question.

Mindset and Action Tips for First-Time Submitters

Feeling anxious about your first rebuttal is completely normal. But it's worth recognizing that a 5/4/4 score with a Meta Reviewer who only flagged two easily-addressed issues is actually a pretty encouraging situation — far better than a full set of 4s or lower.

A few parting reminders:

• Keep a calm, objective tone — avoid anything that sounds emotional or defensive;
• Thank reviewers for their time and feedback, even when you disagree — courtesy always helps;
• Be disciplined with your word count, spending your limited space on the points most likely to change the outcome;
• Whatever happens, the process of writing the rebuttal is itself a valuable piece of research training.

Academic peer review has never been a perfect system — AI-generated reviews, conflicting opinions, and unreasonable demands are all part of the imperfect machinery. Responding rationally and professionally is the best preparation any new researcher can make.