Weakly Supervised Object Localization and Detection: A Survey

Dingwen Zhang, Junwei Han, Gong Cheng, Ming Hsuan Yang

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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.

Original languageEnglish
JournalIEEE transactions on pattern analysis and machine intelligence
DOIs
Publication statusAccepted/In press - 2021

Bibliographical note

Publisher Copyright:
IEEE

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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