[Discussion] How is scale equivariance handled in SOTA Computer Vision Model?

I was reading the DeepLearning Book by Aaron Courville, Ian Goodfellow, and Yoshua Bengio, and they mention the natural equivariance to the translation of convolution when sharing weight. So I wondered about current techniques to handle scale variability.

Do you think weight-sharing and kernel resizing are powerful enough to consider this problem as solved ?

book computer computer vision convolution current deeplearning ian ian goodfellow kernel machinelearning natural reading scale sota think translation vision yoshua yoshua bengio