CVPR2012最勇猛的rebuttal

| Editor’s note: the following is an anonymized letter from a Machine Learning researcher who decided to withdraw his submission from CVPR 2012. The submission received ratings of “Definitely Reject,” “Borderline” and “Weakly Reject.” The letter and the paper reviews are posted here with his permission. |
Hi Serge,
We decided to withdraw our paper #[ID no.] from CVPR “[Paper Title]” by [Author Name] et al.
We posted it on ArXiv: http://arxiv.org/ [ Paper ID] .
We are withdrawing it for three reasons: 1) the scores are so low, and the reviews so ridiculous, that I don’t know how to begin writing a rebuttal without insulting the reviewers; 2) we prefer to submit the paper to ICML where it might be better received; 3) with all the fuss I made, leaving the paper in would have looked like I might have tried to bully the program committee into giving it special treatment.
Getting papers about feature learning accepted at vision conference has always been a struggle, and I’ve had more than my share of bad reviews over the years. Thankfully, quite a few of my papers were rescued by area chairs.
This time though, the reviewers were particularly clueless, or negatively biased, or both. I was very sure that this paper was going to get good reviews because: 1) it has two simple and generally applicable ideas for segmentation (“purity tree” and “optimal cover”); 2) it uses no hand-crafted features (it’s all learned all the way through. Incredibly, this was seen as a negative point by the reviewers!); 3) it beats all published results on 3 standard datasets for scene parsing; 4) it’s an order of magnitude faster than the competing methods.



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