  Recent studies on unsupervised object detection based on spatial attention
have achieved promising results. Models, such as AIR and SPAIR, output "what"
and "where" latent variables that represent the attributes and locations of
objects in a scene, respectively. Most of the previous studies concentrate on
the "where" localization performance; however, we claim that acquiring "what"
object attributes is also essential for representation learning. This paper
presents a framework, GMAIR, for unsupervised object detection. It incorporates
spatial attention and a Gaussian mixture in a unified deep generative model.
GMAIR can locate objects in a scene and simultaneously cluster them without
supervision. Furthermore, we analyze the "what" latent variables and clustering
process. Finally, we evaluate our model on MultiMNIST and Fruit2D datasets and
show that GMAIR achieves competitive results on localization and clustering
compared to state-of-the-art methods.
