The aim of object-centric vision is to construct an explicit representation of the objects in a scene. This representation is obtained via a set of interchangeable modules called \emph{slots} or \emph{object files} that compete for local patches of an image. The competition has a weak inductive bias to preserve spatial continuity; consequently, one slot may claim patches scattered diffusely throughout the image. In contrast, the inductive bias of human vision is strong, to the degree that attention has classically been described with a spotlight metaphor. We incorporate a spatial-locality prior into state-of-the-art object-centric vision models and obtain significant improvements in segmenting objects in both synthetic and real-world datasets. Similar to human visual attention, the combination of image content and spatial constraints yield robust unsupervised object-centric learning, including less sensitivity to model hyperparameters.
@article{arxiv.2305.19550,
title = {Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Prior},
author = {Ayush Chakravarthy and Trang Nguyen and Anirudh Goyal and Yoshua Bengio and Michael C. Mozer},
journal= {arXiv preprint arXiv:2305.19550},
year = {2023}
}