Peer reviews of conference paper submissions is an integral part of the research cycle, though it has unknown origins. For the computer vision community, this process has become significantly more difficult in recent years due to the volume of submissions. For example, the number of submissions to the CVPR conference has tripled in the last ten years. For this reason, the community has been forced to reach out to a less than ideal pool of reviewers, which unfortunately includes uninformed junior graduate students, disgruntled senior graduate students, and tenured faculty. In this work we take the simple intuition that the quality of a paper can be estimated by merely glancing through the general layout, and use this intuition to build a system that employs basic computer vision techniques to predict if the paper should be accepted or rejected. This system can then be used as a first cascade layer during the review pro- cess. Our results show that while rejecting 15% of “good papers”, we can cut down the number of “bad papers” by more than 50%, saving valuable time of reviewers. Finally, we fed this very paper into our system and are happy to report that it received a posterior probability of 88.4% of being “good”.
1. Introduction
Peer reviews of conference paper submissions is an in- tegral part of the research cycle, though it has unknown origins. For the computer vision community, this process has become significantly more difficult in recent years due to the volume of submissions. For example, the number of submission to the CVPR conference has tripled in the last ten years1 (see Fig. 1). For this reason, the commu- nity has been forced to reach out to a less than ideal pool of reviewers, which unfortunately includes uninformed ju- nior graduate students, disgruntled senior graduate students,