Local Scale Equivariance with Latent Deep Equilibrium Canonicalizer
Authors: name: Md Ashiqur Rahman, name: Chiao-An Yang, name: Michael N. Cheng, name: Lim Jun Hao, name: Jeremiah Jiang, name: Teck-Yian Lim, name: Raymond A. Yeh

Scale variation is a fundamental challenge in computer vision. Objects of the
same class can have different sizes, and their perceived size is further
affected by the distance from the camera. These variations are local to the
objects, i.e., different object sizes may change differently within the same
image. To effectively handle scale variations, we present a deep equilibrium
canonicalizer (DEC) to improve the local scale equivariance of a model. DEC can
be easily incorporated into existing network architectures and can be adapted
to a pre-trained model. Notably, we show that on the competitive ImageNet
benchmark, DEC improves both model performance and local scale consistency
across four popular pre-trained deep-nets, e.g., ViT, DeiT, Swin, and BEiT. Our
code is available at https://github.com/ashiq24/local-scale-equivariance.