  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.
