Weakly Supervised Object Localization and Detection: A Survey

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.

arXiv commentIEEE Transactions on Pattern Analysis and Machine Intelligence, Accepted

References in corpus (15)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
• Res2Net: A New Multi-scale Backbone Architecture
• Revisiting Multiple Instance Neural Networks
• Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning
• Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
• Self Paced Deep Learning for Weakly Supervised Object Detection
• Weakly-supervised Discovery of Visual Pattern Configurations
• Tree-Structured Reinforcement Learning for Sequential Object Localization
• Bridging Saliency Detection to Weakly Supervised Object Detection Based on Self-paced Curriculum Learning
• Deep Descriptor Transforming for Image Co-Localization
• Multiple Instance Reinforcement Learning for Efficient Weakly-Supervised Detection in Images
• Optimizing Region Selection for Weakly Supervised Object Detection
• Weakly-Supervised Temporal Localization via Occurrence Count Learning