
Object Detection: An End-to-End Theoretical Perspective - mlwhiz
https://mlwhiz.com/blog/2018/09/22/object_detection/
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mlwhiz
A look at different papers on Object Detection. Mainly Covering RCNN to Faster
RCNN Journey. ANd the intuition behind

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zwieback
It would be interesting to have a section how this approach compares to
classic, non NN, algorithms.

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bostonpete
This approach? Isn't this a summary of deep learning approaches?

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joshvm
Yes, but historically this research is barely seven years old (Alexnet was
published in 2012). There was lots of research into object detection before
deep learning became usable, mostly based around feature engineering
(keypoints, deformable parts, hog classifiers, etc).

Fundamentally the shift is that we've gone from handmade features developed
over years of research to simply letting the model itself learn what's
important. It would be nice to set the scene to realise just how
transformative deep learning has been.

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mlwhiz
Yes. They provide this in the R-CNN paper, "Compared to the multi-feature,
non-linear kernel SVM approach, we achieve a large improvement in mAP, from
35.1% to 53.7% mAP, while also being much faster".

Also in the metrics provided in the paper, the best method was SegDPM had
around 40.4% mAP. Beat it by a pretty large margin at its time.

