World's Best Scientists 2026 revealed!
IEEE

CVPR 2022 : IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Location: New Orleans , United States

Submission deadline: 11/16/2021

Conference dates: 6/21/2022 - 6/21/2022

Research H-index
312

Ranking & Metrics

Discipline name Position Best Scientists Publications D-Index
Electronics and Electrical Engineering 1 123 252 62
Computer Science 1 1831 7017 312
Neuroscience 6 8 12 9

Call for Papers

Review Process: By submitting a paper to CVPR, the authors agree to the review process and understand that papers are processed by the Toronto Paper Matching System (TPMS) to match each manuscript to the best possible area chairs and reviewers.

Confidentiality: The review process of CVPR is confidential. Reviewers are volunteers; they are not part of the CVPR organization and their efforts are greatly appreciated. The practice of keeping all information confidential during the review is part of the standard communication to all reviewers. Misuse of confidential information is a severe professional failure and appropriate measures will be taken when brought to the attention of the CVPR organizers. It should be noted, however, that the organization of CVPR is not and cannot be held responsible for the consequences when reviewers break confidentiality.

Conflict Responsibilities: It is the primary author\'s responsibility to ensure that all authors on their paper have registered their institutional conflicts into the submission system – CMT3 (see details under Domain Conflicts below). If a paper is found to have an undeclared or incorrect institutional conflict, the paper may be summarily rejected. To avoid undeclared conflicts, the author list is considered to be final after the submission deadline and no changes are allowed for accepted papers.

Double blind review: CVPR reviewing is double blind, in that authors do not know the names of the area chair/reviewers of their papers, and the area chairs/reviewers cannot, beyond reasonable doubt, infer the names of the authors from the submission and the additional material. Do not provide information that may identify the authors in the acknowledgments (e.g., co-workers and grant IDs) and in the supplemental material (e.g., titles in the movies, or attached papers). Also do not provide links to websites that identify the authors. Violation of any of these guidelines may lead to rejection without review. If you need to cite a different paper of yours that is being submitted concurrently to CVPR or another venue, the authors should (1) cite these papers; (2) argue in the body of your paper why your CVPR paper is non-trivially different from these concurrent submissions; and (3) include anonymized versions of those papers in the supplemental material.

Plagiarism: Plagiarism consists of appropriating the words or results of another, without credit. CVPR 2022\'s policy on plagiarism is to refer suspected cases to the IEEE Intellectual Property office, which has an established mechanism for dealing with plagiarism and wide powers of excluding offending authors from future conferences and from IEEE journals. You can find information on this office, their procedures, and their definitions of five levels of plagiarism at this webpage. We will be actively checking for plagiarism. Furthermore, the paper matching system is quite accurate. As a result, it regularly happens that a paper containing plagiarized material goes to a reviewer from whom material was plagiarized; experience shows that such reviewers pursue plagiarism cases enthusiastically.

Dual/Double Submissions: The goals of CVPR are to publish exciting new work for the first time and to avoid duplicating the effort of reviewers. By submitting a manuscript to CVPR, the authors acknowledge that it has not been previously published or accepted for publication in substantially similar form in any peer-reviewed venue including journal, conference or workshop, or archival forum. Furthermore, no publication substantially similar in content has been or will be submitted to this or another conference, workshop, or journal during the review period. Violation of any of these conditions will lead to rejection, and will be reported to the other venue to which the submission was sent.

A publication, for the purposes of this policy, is defined to be a written work longer than four pages (excluding references) that was submitted for review by peers for either acceptance or rejection, and, after review, was accepted. In particular, this definition of publication does not depend upon whether such an accepted written work appears in a formal proceedings or whether the organizers declare that such work “counts as a publication”.

As per PAMI TC motion, the above definition does not consider an arXiv.org pre-print as a publication because it cannot be rejected. It also excludes university technical reports, which are typically not peer reviewed. However, this definition of publication does include peer-reviewed workshop papers, even if they do not appear in a proceedings, if their length is more than four pages (excluding citations). Given this definition, any submission to CVPR should not have substantial overlap with prior publications or other concurrent submissions.

A submission with substantial overlap is one that shares 20 percent or more material with previous or concurrently submitted publications. Authors are encouraged to contact the Program Chairs about clarifications on borderline cases.

Note that a technical report (departmental, arXiv.org, etc.) version of the submission that is put up without any form of direct peer-review is NOT considered prior art and should NOT be cited in the submission.

Attendance responsibilities: The authors agree that if the paper is accepted, at least one of the authors will register for the conference and present the paper there.

Publication: All accepted papers will be made publicly available by the Computer Vision Foundation (CVF) two weeks before the conference. Authors wishing to submit a patent understand that the paper\'s official public disclosure is two weeks before the conference or whenever the authors make it publicly available, whichever is first. The conference considers papers confidential until published two weeks before the conference, but notes that multiple organizations will have access during the review and production processes, so those seeking patents should discuss filing dates with their IP council. The conference assumes no liability for early disclosures. More information about CVF is available at http://www.cv-foundation.org/.

Publicity, social media: Papers submitted to CVPR must not be discussed with the press until they have been officially accepted for publication. Work explicitly identified as a CVPR submission also may not be advertised on social media. Please see the FAQ section for more details. Violations may result in the paper being summarily rejected or removed from the conference and proceedings.

Authors acting as reviewers: Given the growth of the number of paper submissions, we expect all authors to be willing to serve as reviewers as well. With a large enough pool of reviewers, we expect that reviewers will be sent on the order of five papers to review. Our timeline expects that papers will go to reviewers about December 16, 2021, and reviews should be returned on or before January 14, 2022.

Overview

This Computer Science Conference Ranking presents a comprehensive list of scientific conferences in the field of Computer Science, meticulously curated to assist the academic community, industry professionals, and research institutions in identifying leading venues for scholarly contributions. The ranking has been developed by Research.com, a prominent platform renowned for providing trusted and in-depth data on scientific research and contributions across all major disciplines since 2014.

Each conference’s position in this ranking is determined using Research.com’s proprietary bibliometric score, a carefully designed metric that synthesizes the estimated h-index with the number of leading scientists who have contributed to the conference during the past three years. This multifaceted approach ensures objective evaluation reflecting both scientific impact and the prestige of contributors.

The Impact Score values incorporated into this ranking were collected on 2024-11-27, ensuring that the data reflects the most recent and relevant scientific developments and participation trends. The rigorous ranking process involved the examination of over 2,742 conferences, all of which were selected following an extensive vetting process. This procedure included the analysis of more than 148,739 peer-reviewed scientific documents published over the last three years by 13,184 leading and well-respected Computer Science scholars.

The methodology guiding this ranking is the result of expert-driven and analytically robust procedures designed to maintain the highest standards of credibility and transparency. For those seeking a deeper understanding of the processes and metrics used, detailed information is available on our Methodology Page.

Papers citation over time

A key indicator for each conference is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

The top authors publishing at Computer Vision and Pattern Recognition (based on the number of publications) are:

  • Luc Van Gool (130 papers) published 16 papers at the last edition, 6 more than at the previous edition,
  • Thomas S. Huang (107 papers) published 1 paper at the last edition, 4 less than at the previous edition,
  • Ming-Hsuan Yang (101 papers) published 4 papers at the last edition, 5 less than at the previous edition,
  • Marc Pollefeys (98 papers) published 9 papers at the last edition, 2 more than at the previous edition,
  • Larry S. Davis (95 papers) published 8 papers at the last edition, 2 more than at the previous edition.

The overall trend for top authors publishing at this conference is outlined below. The chart shows the number of publications at each edition of the conference for top authors.

Only papers with recognized affiliations are considered

The top affiliations publishing at Computer Vision and Pattern Recognition (based on the number of publications) are:

  • Microsoft (756 papers) published 60 papers at the last edition, 6 less than at the previous edition,
  • Carnegie Mellon University (671 papers) published 49 papers at the last edition, 2 less than at the previous edition,
  • Chinese Academy of Sciences (534 papers) published 128 papers at the last edition, 34 more than at the previous edition,
  • Massachusetts Institute of Technology (466 papers) published 36 papers at the last edition, 3 more than at the previous edition,
  • Tsinghua University (445 papers) published 112 papers at the last edition, 41 more than at the previous edition.

The overall trend for top affiliations publishing at this conference is outlined below. The chart shows the number of publications at each edition of the conference for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions at the conference edition to all articles published within that conference. The best research institutions were selected based on the largest number of articles published during all editions of the conference.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

During the most recent 2021 edition, 3.28% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 27.47% were posted by at least one author from the top 10 institutions publishing at the conference. Another 12.14% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 21.38% of all publications and 39.02% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of conferences they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same conference from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the conference in relation to all participants in a given year.

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing at a conference. The index includes the authors publishing at the last edition of a conference, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

  • Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
  • Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
  • Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
  • Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
  • Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Related Online Degrees & Career Pathways

Pursuing a degree in Computer Science opens the door to various specialized fields and career paths. For those looking to expedite their education, an accelerated computer science degree online can help students enter the workforce faster without compromising quality.

Specializations like data science offer promising opportunities, and many are interested in finding the most cost-effective ways to advance. Exploring the cheapest data science masters in usa is a great way to identify budget-friendly programs that don’t sacrifice reputation or rigor.

For those fascinated by emerging technology, earning an AI degree is a strategic move. There are comprehensive options that combine flexibility with advanced curriculum, as seen in top-ranked ai degree programs available online.

Cybersecurity is another critical area in the tech industry, with rapid growth and demand for professionals. If time is a constraint, students should explore the fastest way to get a cybersecurity degree online to jumpstart their careers quickly.

Best Scientists who published in this Conference

Related Articles

Recently Published Articles