# WACV 2027 review form > Simulate WACV 2027-style reviews for the Algorithms, Applications or Evaluations & Datasets track: Summary, Strengths, Weaknesses, a five-level rating with justification, and confidence; round-1 papers can get Revise and Resubmit. - Venue: IEEE/CVF Winter Conference on Applications of Computer Vision - Form status: modeled, not official. WACV's OpenReview form is not public. Fields follow the CVF pattern and the tracks and round-1 outcomes come from the official 2027 guides; the five-level rating is modeled from labels used in WACV 2026 AC templates (Strong Reject / Reject / Neutral / Accept), not official wording. - Official source: https://wacv.thecvf.com/Conferences/2027/ReviewerGuidelines - Overall score: Rating, 1–5 - Rough acceptance bar (our simulation): mean ≥ 3.5 - Web page: https://reviewpaper.ai/venues/wacv ## How reviewers read the paper WACV runs two rounds. Round-1 papers get Accept, Revise and Resubmit (a revised paper plus one-page rebuttal returns to the same reviewers about two weeks later) or Reject; round-2 papers are accepted or rejected with no rebuttal. Reviewers argue strengths and weaknesses in detail and judge Applications papers on systems-level innovation, domain novelty and comparative assessment rather than algorithmic novelty alone. - Refer to "the paper" — never "you" or "the authors". - No belittling or sarcastic remarks; professional, courteous tone with concrete suggestions. - Back up any "done before" claim with specific references and explain how close the prior work is. - Not beating state of the art, or not citing/beating an arXiv-only paper, is not on its own grounds for rejection. - Do not request substantial new experiments for the rebuttal. - Use neutral references ("the paper", "R1"); do not ask the paper to cite your own work. **Where the bar sits.** WACV 2026 accepted 832 of 2,550 submissions (33%): round 1 38% (84 direct accepts, 439 of 736 revisions accepted), round 2 26%. No score statistics are public; on this modeled 1–5 scale a mean of ≥ 3.5 is a rough proxy for acceptance or a revise-and-resubmit invitation. ## Reviewer form, field by field ### 1. Track Type: single choice. The track the paper was most plausibly submitted to. Applications papers are judged on systems-level innovation, novelty of the application domain and comparative assessment, not solely on algorithmic novelty; Evaluations & Datasets papers are judged like the NeurIPS E&D track. - Algorithms - Applications - Evaluations & Datasets ### 2. Summary Type: free text. The key ideas, contributions and their significance, in your own words. ### 3. Strengths Type: list of points. What gives the paper value, each point argued with specifics; short bullet fragments are not acceptable. A single short sentence or a few words do not suffice. ### 4. Weaknesses Type: list of points. What detracts from the contributions, each argued with specifics. A novelty concern must cite the specific prior work. A single short sentence or a few words do not suffice. ### 5. Rating Type: score, 1–5. Overall rating for the track's criteria. - **5**: Strong Accept - **4**: Accept - **3**: Neutral - **2**: Reject - **1**: Strong Reject ### 6. Justification And Requested Revisions Type: free text. Connect the rating to the assessment. For round-1 papers, list precisely the revisions that a two-week Revise-and-Resubmit could address, and which issues (if any) could not be fixed in that window. ### 7. Confidence Type: score, 1–5. How certain you are about your assessment. - **5**: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully. - **4**: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. - **3**: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked. - **2**: You are willing to defend your assessment, but it is quite likely that you did not understand the central parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked. - **1**: Your assessment is an educated guess. The submission is not in your area or the submission was difficult to understand. Math/other details were not carefully checked. ## Area chair meta-review WACV AC pairs decide; overruling all three reviewers is not acceptable, and departing from the reviewer majority must be raised with the PCs. Recommendation options: Accept · Revise and Resubmit · Reject ## WACV author requirements 1. Main text within 8 pages plus references; abstract at most 5,000 characters 2. Track chosen: Algorithms, Applications, or Evaluations & Datasets 3. Anonymized submission; supplementary material adds no results on new datasets or from an improved method 4. Reproducibility checklist (recommended) ## LLM policy WACV lists reviews generated by LLMs as highly irresponsible, which can lead to desk rejection of the reviewer's own papers. Official policy: https://wacv.thecvf.com/Conferences/2027/ReviewerGuidelines --- Simulate reviews of your own draft in this venue's form: https://reviewpaper.ai/new?venue=wacv (3 free reviews). Sample report: https://reviewpaper.ai/sample. reviewpaper.ai is for authors checking their own unpublished drafts. If you are an assigned reviewer, follow your venue's LLM policy: most venues forbid putting a submission you are reviewing into any external AI tool. 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