A Novel Laguerre Voronoi Diagram Token Filtering Strategy for Computer Vision Transformers

Transformer architectures emerged as frontrunning approaches for computer vision classification tasks. Although transformers are faster than recurrent networks, they can become time and memory-inefficient. This paper proposes a novel filtering strategy based on Laguerre Voronoi diagram-based feature similarity measure. The approach allows to choose the most influential tokens and thus to reduce redundant information processing and extra computational costs. The filtering strategy can significantly reduce computation and memory overhead of vision transformers, depending on the number of patches to process. By comparing the proposed strategy with existing token filtering strategies, we report that the proposed architecture performs better than the comparators while reducing the computational overhead.