Unsupervised Multiple-Instance Learning for Instance Search

Traditional supervised Multiple-Instance Learning (MIL) has served as an important tool for a wide range of vision applications, for instance, image classification, object detection, and visual tracking. In this paper, we move forward one step further to tackle unsupervised computer vision problems by proposing an unsupervised multiple-instance learning algorithm, termed UnMIL. Different from classical MIL, our proposed unsupervised MIL does not require any manual annotations on neither bags nor instances. Given a collection of bags without any labels, our goal is to jointly optimize the bag label and instance label in a unified framework under the constraint of Noisy-OR model. The proposed UnMIL can be easily applied to object discovery in wild images by treating the object proposals extracted from images as instances and the according images as bags. Extensive experiments on MUSK1 MUSK2, which is popularly used in MIL literature, on Oxford5k dataset for instance search, and on Object Discovery dataset for object co-localization, demonstrate the effectiveness of the proposed UnMIL.