SpaceJAM: a Lightweight and Regularization-Free Method for Fast Joint Alignment of Images

Nir Barel, Ron Shapira Weber, Nir Mualem, Shahaf E. Finder, Oren Freifeld

Abstract:

Abstract. The unsupervised task of Joint Alignment (JA) of images is
beset by challenges such as high complexity, geometric distortions, and
convergence to poor local or even global optima. Although Vision Transformers (ViT) have recently provided valuable features for JA, they fall
short of fully addressing these issues. Consequently, researchers frequently
depend on expensive models and numerous regularization terms, resulting
in long training times and challenging hyperparameter tuning. We introduce the Spatial Joint Alignment Model (SpaceJAM), a novel approach
that addresses the JA task with efficiency and simplicity. SpaceJAM
leverages a compact architecture with only ∼16K trainable parameters
and uniquely operates without the need for regularization or atlas maintenance. Evaluations on SPair-71K and CUB datasets demonstrate that
SpaceJAM matches the alignment capabilities of existing methods while
significantly reducing computational demands and achieving at least a
10x speedup. SpaceJAM sets a new standard for rapid and effective image
alignment, making the process more accessible and efficient. Our code is
available at: https://bgu-cs-vil.github.io/SpaceJAM/.