"Feature Pyramid Network" is a network that is used for feature extraction. Since it is pyramid in shape, it might be called so.
Consider the following excerpts from two different sources
A Feature Pyramid Network, or FPN, is a feature extractor that takes a single-scale image of an arbitrary size as input, and outputs proportionally sized feature maps at multiple levels, in a fully convolutional fashion. This process is independent of the backbone convolutional architectures. It, therefore, acts as a generic solution for building feature pyramids inside deep convolutional networks to be used in tasks like object detection.
FPN composes of a bottom-up and a top-down pathway. The bottom-up pathway is the usual convolutional network for feature extraction. As we go up, the spatial resolution decreases. With more high-level structures detected, the semantic value for each layer increases.
Both the sources are giving a strong sense that the phrase "Feature Pyramid Network" must be used for CNN's only as it is used mainly intended for object detection. But the name and purpose suggest to me that any ANN that is pyramid in shape can be attributed as "Feature Pyramid Network" since any ANN tries to extract features only in the general sense.
Am I true? Can I use the phrase for any arbitrary ANN that is pyramid in shape or is it an exclusive term of CNNs in computer vision only?