Local Scale Equivariance with Latent Deep Equilibrium Canonicalizer
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.
68a69d2b9e4b49496aac6a8a
Md Ashiqur Rahman
claimed_verified
2025-08-21T09:15:51.907Z
661e07e02a8496916011c08a
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/6vIkL4LJ0bLeJL99E20F_.jpeg
Md Ashiqur Rahman
user
ashiq24
68a69d2b9e4b49496aac6a8b
Chiao-An Yang
68a69d2b9e4b49496aac6a8c
Michael N. Cheng
68a69d2b9e4b49496aac6a8d
Lim Jun Hao
68a69d2b9e4b49496aac6a8e
Jeremiah Jiang
68a69d2b9e4b49496aac6a8f
Teck-Yian Lim
68a69d2b9e4b49496aac6a90
Raymond A. Yeh
Local Scale Equivariance with Latent Deep Equilibrium Canonicalizer
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.
A deep equilibrium canonicalizer (DEC) enhances local scale equivariance in deep networks, improving performance and consistency on ImageNet.
deep equilibrium canonicalizer DEC local scale equivariance ImageNet ViT DeiT Swin BEiT