# ECCV 2026 review form > Simulate ECCV 2026-style reviews: Summary, Strengths and Weaknesses judged against the paper's contribution type, a rating with justification, and additional comments, in the CVF/ECVA depersonalized voice. - Venue: European Conference on Computer Vision - Form status: modeled, not official. ECCV's OpenReview form is not public. The sections (Summary, Strengths, Weaknesses, Rating and Justification, Additional Comments) and contribution types come from the official 2026 reviewer tutorial slides; the numeric rating and confidence scales are modeled on CVPR, not official. - Official source: https://eccv.ecva.net/Conferences/2026/ReviewerGuide - Overall score: Rating, 1–5 - Rough acceptance bar (our simulation): mean ≥ 3.5 - Web page: https://reviewpaper.ai/venues/eccv ## How reviewers read the paper ECCV 2026 reviewers write a summary ("your abstract of the paper"), substantive strengths and weaknesses judged against the paper's declared contribution type (short bullet lists are not acceptable), a rating with a justification that weighs the key strengths and weaknesses, and minor additional comments; after the rebuttal they give a final recommendation and justification. Avoid the novelty fallacy, pure opinions, blank assertions, and demands to beat arXiv papers. - 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. - Avoid the novelty fallacy, blank assertions and pure opinions; strengths and weaknesses must be argued, not listed. **Where the bar sits.** ECCV 2026 accepted about 27.5% of ~10,500 submissions (secondary sources; ECCV 2024: 27.9%). No score statistics are public; on this modeled 1–5 scale a mean of ≥ 3.5 is a rough proxy for acceptance. ## Reviewer form, field by field ### 1. Contribution Type Type: single choice. The contribution type the authors most plausibly declared. Strengths and weaknesses are judged against it (e.g. a Datasets/Benchmarks paper is not expected to propose a novel method); any departure must be explained in the justification. - Algorithms/General - Theory/Foundational - Applied/Systems - Datasets/Benchmarks - Concept & Feasibility ### 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. Judge value against the declared contribution type. ### 4. Weaknesses Type: list of points. What detracts from the contributions, each argued with specifics. A novelty concern must cite the specific prior work. Judge against the declared contribution type. ### 5. Rating Type: score, 1–5. Preliminary rating before the rebuttal. - **5**: Strong Accept - **4**: Weak Accept - **3**: Borderline - **2**: Weak Reject - **1**: Reject ### 6. Justification Of Rating Type: free text. Why the paper should or should not be accepted: which strengths and weaknesses matter most and how they weigh against each other given the contribution type, and what the rebuttal should address. ### 7. Additional Comments Type: list of points. Minor suggestions, questions and corrections that are not crucial to the recommendation. ### 8. 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 ECCV ACs decide accept/reject and nominate orals and award candidates; accepting against unanimous reviewers needs exceptional justification. Do not lean on reviewer confidence scores. Recommendation options: Award candidate · Oral · Poster · Reject ## ECCV author requirements 1. Main paper at most 14 pages excluding references 2. Contribution type declared (Algorithms/General, Theory/Foundational, Applied/Systems, Datasets/Benchmarks, Concept & Feasibility) 3. Anonymized; no external links that expand content, and no prompt injection 4. Datasets/Benchmarks papers: data public by camera-ready 5. No citations to non-existent material ## LLM policy ECCV 2026 does not allow LLMs to write reviews or meta-reviews, locally or via API, and no substantial paper or review content may be shared with an LLM (including for translation). Grammar checkers and background research are allowed. Violations can desk-reject the reviewer's own papers. Official policy: https://eccv.ecva.net/Conferences/2026/ReviewerGuide --- Simulate reviews of your own draft in this venue's form: https://reviewpaper.ai/new?venue=eccv (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. Not affiliated with any conference.