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Recent studies on unsupervised object detection based on spatial attentionhave achieved promising results. Models, such as AIR and SPAIR, output "what"and "where" latent variables that represent the attributes and locations ofobjects in a scene, respectively. Most of the previous studies concentrate onthe "where" localization performance; however, we claim that acquiring "what"object attributes is also essential for representation learning. This paperpresents a framework, GMAIR, for unsupervised object detection. It incorporatesspatial attention and a Gaussian mixture in a unified deep generative model.GMAIR can locate objects in a scene and simultaneously cluster them withoutsupervision. Furthermore, we analyze the "what" latent variables and clusteringprocess. Finally, we evaluate our model on MultiMNIST and Fruit2D datasets andshow that GMAIR achieves competitive results on localization and clusteringcompared to state-of-the-art methods.

GMAIR: Unsupervised Object Detection Based on Spatial Attention and Gaussian Mixture