Unsupervised object discovery aims to localize objects in images, while removing the dependence on annotations required by most deep learning-based methods. To address this problem, we propose a fully unsupervised, bottom-up approach, for multiple objects discovery. The proposed approach is a two-stage framework. First, instances of object parts are segmented by using the intra-image similarity between self-supervised local features. The second step merges and filters the object parts to form complete object instances. The latter is performed by two CNN models that capture semantic information on objects from the entire dataset. We demonstrate that the pseudo-labels generated by our method provide a better precision-recall trade-off than existing single and multiple objects discovery methods. In particular, we provide state-of-the-art results for both unsupervised class-agnostic object detection and unsupervised image segmentation.
翻译:未受监督的物体发现旨在将物体定位在图像中,同时消除对大多数深层学习方法所要求的说明的依赖性。为了解决这一问题,我们提出一种完全不受监督的、自下而上的方法,用于多天体发现。拟议的方法是一个两阶段框架。首先,物体部件的情况通过使用图像内部自监督的本地特性之间的相似性进行分割。第二步合并和过滤对象部件,形成完整的天体实例。后一步由两个CNN模型进行,该模型从整个数据集中收集物体的语义信息。我们证明,我们的方法产生的伪标签比现有的单项和多天体发现方法提供了更好的精确回回转交换。特别是,我们为非监督的类敏感物体探测和不受监督的图像分割提供了最先进的结果。