I feel like this might be a stupid question, but I’ve been doing some research and while I understand the design of convolutional neural networks, I’m having trouble understanding how they work for classifying multiple types of objects.

From what I know, CNNs work by adjusting their filters to be able to recognize an object. However, if there is more than one type of object that the filters need to be adjusted for, how is this done? Is it that we simply add more neurons to each layer to account for additional object filters? Or are the learned filters in a sense “averaged” across the types of images?