IEEE Trans Pattern Anal Mach Intell. 2022 Sep;44(9):5866-5885. doi: 10.1109/TPAMI.2021.3074313. Epub 2022 Aug 4.
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.
作为计算机视觉领域的一个新兴且具有挑战性的问题，弱监督目标定位和检测对于开发新一代计算机视觉系统起着重要作用，在过去十年中受到了广泛关注。随着方法的提出，对这些主题进行全面调查非常重要。在这项工作中，我们回顾了（1）经典模型，（2）来自现成的深度网络的特征表示方法，（3）仅基于深度学习的方法，以及（4）在该领域广泛使用的公开可用数据集和标准评估指标。我们还讨论了该领域的关键挑战、该领域的发展历史、每个类别的方法的优缺点、不同类别之间的方法之间的关系、弱监督目标定位和检测方法的应用以及进一步推动该研究领域发展的潜在未来方向。
