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.

该论文提出了一个基于无监督学习的物体发现方法，其中包括了基于显著性的边界提取算法以及采用层次结构与两阶段策略提高发现效率。通过使用小型随机图片集和整个图片集合，该方法能够发现每张图片中的多个目标，实现了对面向真实大规模图像数据集的无监督图像解释的重要的一步。