Thanks for the paper!
Skimming through it, I think their method uses dilated convolutions to get a spatially bigger activation map in Group5 of a ResNet. However, for classification they still seem to use global average pooling.
From section 2:

The output of G5 in a DRN is 28 × 28. Global average pooling therefore takes in 2**4 times more values, which can help the classifier recognize objects that cover a smaller number of pixels in the input image and take such objects into account in its prediction.

Figure 2 is related to a localization use case, i.e. if you have some kind of localization/segmentation target.
Also, I couldn’t find any transposed convolutions in the paper, just dilated convolutions.
Is this a misunderstanding or how would you like to use transposed convolutions for this method?