Object detection has undergone a dramatic and fundamental shift. I’m not talking about deep learning here – deep learning is really more about classification and specifically about feature learning. Feature learning has begun to play and will continue to play a critical role in machine vision. Arguably in a few years we’ll have a diversity of approaches for learning rich features hierarchies from large amounts of data; it’s a fascinating topic. However, as I said, this post is not about deep learning.
Rather, perhaps an even more fundamental shift has occurred in object detection: the recent crop of top detection algorithms abandons sliding windows in favor of segmentation in the detection pipeline. Yes, you heard right, segmentation!
First some evidence for my claim. Arguably the three top performing object detection systems as of the date of this post (12/2013) are the following:
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