I believe there would be significant drop in performance of ResNet. Convolutional kernels will be passing mostly over zeros, thus I would expect feature vector to be mostly zeroed too, but it’s got to be checked.
As there are a lot of small-sized superpixels I would probably approach this problem with 0-padding each superpixel to a smallest rectangle and training a small CNN for feature extraction. Siamese architectures might be reasonable for training in this case, but it depends on the data you have at hand and what you want your descriptors to do.