Weakly Supervised Object Localization and Detection: A Survey

TL;DR

This survey comprehensively reviews weakly supervised object localization and detection methods, covering classic models, deep learning approaches, datasets, evaluation metrics, challenges, and future directions in the field.

Contribution

It provides an organized overview of existing methods, their advantages and disadvantages, and discusses the development history and future prospects of weakly supervised object localization and detection.

Findings

Summarizes key challenges in the field.

Categorizes methods into four main groups.

Highlights datasets and evaluation metrics used.

Abstract

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.