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Paper Gestalt
Carven von Bearnensquash
Background
• Peer review imperfect review process• Growth in the volume of submissions, tripled
over the last 10 years• Less than ideal pool of reviewers• General layout of a paper
Abstract
• Intuition: Quality of paper general layout of the paper
• Computer vision techniques to predict if the paper should be accepted
• Result: reject 15% of good papers, cut down the number of “bad papers” by more than 50%
Related work
• Unique work• Text based – biased to certain terms:
“boosting”, “svm”, “crf”, ignores rich visual information
• No previous work known
Approach
• Formulated as a binary classification task• Training data set of example-label pairs, {(x1;
y1); (x2; y2); ...(xn; yn)}, Xi: feature values for paper i, Yi: binary label, “good” or “bad”
• Goal: learn a function f: X {0, 1}
Approach
• Adaboost
• Select feature classifierwith lowest error rate, increase weight of mis-classified data
Approach
• Empirical error is bounded by
• More math: Include Maxwell’s equations in the paper
• Equations improvepaper gestalt
Features
• gradient, texture, color and spatial information
• LUV histograms, Histograms of Oriented Gradients and gradient magnitude.
Experiments - Data Acquisition
• Accepted papers from CVPR 2008, ICCV 2009, and CVPR 2009 as positive examples #1196
• Workshop papers from these same conferences as an approximation as negative examples #665
• Papers converted to images, resized and padded with blank pages.
• 25% testing and 75% training
Experiments -
• Assuming that rejecting 15% of good papers is acceptable, we can cut bad papers in half
Experiments
• “we’re not sure what this figure reveals”• bar plots are particularly aesthetically pleasing
Experiments – good examples
Experiments – bad examples
Experiments – the paper itself
• The system reported a posterior probability of 88.4%, which reassured us that this paper is fit for the CVPR conference.
Conclusions
• The quality of a computer vision paper can be estimated well by basic visual features
• A real-time system to predict weather a paper should be accepted or rejected
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