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Paper Gestalt

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.

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Paper Gestalt

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  1. Paper Gestalt Carven von Bearnensquash

  2. 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

  3. 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%

  4. Related work • Unique work • Text based – biased to certain terms: “boosting”, “svm”, “crf”, ignores rich visual information • No previous work known

  5. 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}

  6. Approach • Adaboost • Select feature classifierwith lowest error rate, increase weight of mis-classified data

  7. Approach • Empirical error is bounded by • More math: Include Maxwell’s equations in the paper • Equations improvepaper gestalt

  8. Features • gradient, texture, color and spatial information • LUV histograms, Histograms of Oriented Gradients and gradient magnitude.

  9. 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

  10. Experiments - • Assuming that rejecting 15% of good papers is acceptable, we can cut bad papers in half

  11. Experiments • “we’re not sure what this figure reveals” • bar plots are particularly aesthetically pleasing

  12. Experiments – good examples

  13. Experiments – bad examples

  14. 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.

  15. 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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