What You See Is What You Transform: Foveated Spatial Transformers as a Bio-Inspired Attention Mechanism

Abstract

Convolutional Neural Networks have been considered the go-to option for object recognition in computer vision for the last couple of years. However, their invariance to object’s translations is still deemed as a weak point and remains limited to small translations only via their max-pooling layers. One bio-inspired approach considers the What/Where pathway separation in Mammals to overcome this limitation. This approach works as a nature-inspired attention mechanism, another classical approach of which is Spatial Transformers. These allow an adaptive endto-end learning of different classes of spatial transformations throughout training. In this work, we overview Spatial Transformers as an attention-only mechanism and compare them with the What/Where model. We show that the use of attention restricted or ``Foveated’’ Spatial Transformer Networks, coupled alongside a curriculum learning training scheme and an efficient log-polar visual space entry, provides better performance when compared to the What/Where model, all this without the need for any extra supervision whatsoever.

Type
Publication
IJCNN 2022 : International Joint Conference on Neural Networks

IJCNN page: https://www.techrxiv.org/articles/preprint/What_You_See_Is_What_You_Transform_Foveated_Spatial_Transformers_as_a_bio-inspired_attention_mechanism/16550391/1

Laurent U Perrinet
Laurent U Perrinet
Researcher in Computational Neuroscience

My research interests include Machine Learning and computational neuroscience applied to Vision.