I'd be interested to see a class activation map to see if there's anything in it, probably not...
Does it mean simply "what part of the image (or in aggregate, images) carried the strongest signal for categorization"? Or can it also tell you something about the feature set?
Would you suggest and good introductory example to the concept? This was the best I found in my search https://jacobgil.github.io/deeplearning/class-activation-map...
"A class activation map for a particular category indicates the discriminative image regions used by the CNN to identify that category"
So, it's mostly what you said, but the maps you get are specific to the particular trained network you're using; the class activation map tells you, for a given network, for a given image, for a given class, which image regions did that network consider relevant to that class.
For learning more about interpreting neural nets I would highly recommend the posts on distill.pub; https://distill.pub/2018/building-blocks/ and https://distill.pub/2019/activation-atlas/ are probably most pertinent.