hi, I have this data where for each sample there is a image list [img1, img2, img3, …]. For each image, the shape is different, and for each sample the number of images also varies.
I could come up with some conv net where different sized image could be reduce to a fix length representation (by adaptive pooling). But the only solution for number of images i can think of is just sample them to be the same.
Could anyone give me some idea that uses some neural network instead of sampling to deal with this issue?