  This paper addresses the problem of discovering the objects present in a
collection of images without any supervision. We build on the optimization
approach of Vo et al. (CVPR'19) with several key novelties: (1) We propose a
novel saliency-based region proposal algorithm that achieves significantly
higher overlap with ground-truth objects than other competitive methods. This
procedure leverages off-the-shelf CNN features trained on classification tasks
without any bounding box information, but is otherwise unsupervised. (2) We
exploit the inherent hierarchical structure of proposals as an effective
regularizer for the approach to object discovery of Vo et al., boosting its
performance to significantly improve over the state of the art on several
standard benchmarks. (3) We adopt a two-stage strategy to select promising
proposals using small random sets of images before using the whole image
collection to discover the objects it depicts, allowing us to tackle, for the
first time (to the best of our knowledge), the discovery of multiple objects in
each one of the pictures making up datasets with up to 20,000 images, an over
five-fold increase compared to existing methods, and a first step toward true
large-scale unsupervised image interpretation.
