  Recent years have witnessed a significant increase in the number of paper
submissions to computer vision conferences. The sheer volume of paper
submissions and the insufficient number of competent reviewers cause a
considerable burden for the current peer review system. In this paper, we learn
a classifier to predict whether a paper should be accepted or rejected based
solely on the visual appearance of the paper (i.e., the gestalt of a paper).
Experimental results show that our classifier can safely reject 50% of the bad
papers while wrongly reject only 0.4% of the good papers, and thus dramatically
reduce the workload of the reviewers. We also provide tools for providing
suggestions to authors so that they can improve the gestalt of their papers.
