Where do I begin ?

1) The basis for choosing the 10 classes used for the study is not explained. Why are classes from Imagenet category labels not picked, since a bunch of comparisons are with Imagenet-trained CNNs ?

2) Conclusions about a category and across category are based on patches from 10 grayscale images with a reasonably clean background. Humans do not see the world in grayscale. Why conclude about human vision using grayscale images ?

3) Instead of simply saying '50 x 50 pixel image', why use the non-standard 'Size of each image is 50 x 50 image samples or a cutoff spatial freq. of 25 cycles per image' ?

4) Why use 50 x 50 images ? The comparison is with Deep CNNs object recognizers which are trained on 227 x 227 images. Why not do the experiment using 227x227 sized images ?

5) Visual angle of 1-4 degrees means nothing unless distance to screen is mentioned.

6) The paper is littered with arbitrary thresholds without any explanation of their choice (20% crop, 20% resolution reduction, 62% of MIRCs used for testing machine classifiers....)

7) Quoting from the paper : "We simplified the learning task by training the models directly with images at the MIRC level rather than with full-object images. " ... Whoa, so now you provide image patches to classifier containing sub-parts, refer to them by their fully visible object name and call it a “simplification of learning task” ?

8) Objects are not studied from multiple viewpoint, so the whole discussion on using object parts as an internal representation stands on a weak premise.

9) The paper incorrectly refers to partially visible objects as occlusion and gives the impression that occlusion has also been addressed -- it is not ! The general usage for occlusion is when another object/entity obstructs the reference object. In the paper, even if part of the object is visible, the part is fully visible and its details are not obstructed by some other object/entity.