As an emerging and challenging problem in the computer vision community, weakly supervised object localization and detection plays an important role for developing new generation computer vision systems and has received significant attention in the past decade. As methods have been proposed, a comprehensive survey of these topics is of great importance. In this work, we review (1) classic models, (2) approaches with feature representations from off-the-shelf deep networks, (3) approaches solely based on deep learning, and (4) publicly available datasets and standard evaluation metrics that are widely used in this field. We also discuss the key challenges in this field, development history of this field, advantages/disadvantages of the methods in each category, the relationships between methods in different categories, applications of the weakly supervised object localization and detection methods, and potential future directions to further promote the development of this research field.

本文综述了弱监督物体定位和检测领域中的经典模型、深度网络特征表示法、基于纯深度学习的方法及公共数据集和标准评估指标等内容。同时也讨论了该领域的发展历程、方法类别间的关系、应用以及未来研究方向等。

弱监督目标定位与检测综述