Abstract
The task of object discovery is to gure out common categories of objects in multiple images without prior knowledge of object categories. It is considered as very challenging computer vision problem. Given a set of images, we aim to identify and localize the common objects. The common objects may vary in scales, poses, appearances, and with occlusions, and these variations make the task of object discovery more diff cult. Conventional solutions tackle the problem with the aid of human intervention. In this work, we present an unsupervised method to learn effective features for object discovery.