Hi,
Is there any CNN that is able to learn features representation for each superpixel of the image. In general, we use SLIC algorithm to obtain the superpixels and compute the HOG of that image than associate a hog vector to each superpixel. However, HOG are hand-crafted features. I would like to associate a learned feature representation for each superpixel (rather than hog). So my question, is there any CNN that can learns  features representation for each superpixel  and the superpixel themselves ?
You may say just use Faster rcnn to localize objects then you get the bounding boxes and features represetnation. However, it doesn’t apply for my case. l don’t not necessary have objects but patches.
Thank you