Weakly Supervised Object Localization and Detection: A Survey
It serves as a resource for researchers in computer vision by summarizing advancements in an emerging area, but it is incremental as a survey paper.
This paper provides a comprehensive survey of weakly supervised object localization and detection, reviewing classic models, deep learning approaches, datasets, and evaluation metrics, and discussing challenges, history, and future directions in the field.
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.