Deep Learning for Weakly-Supervised Object Detection and Object Localization: A Survey

• URL: http://arxiv.org/abs/2105.12694v1
• Date: Wed, 26 May 2021 17:15:53 GMT
• Title: Deep Learning for Weakly-Supervised Object Detection and Object Localization: A Survey
• Authors: Feifei Shao, Long Chen, Jian Shao, Wei Ji, Shaoning Xiao, Lu Ye, Yueting Zhuang, Jun Xiao
• Abstract summary: Weakly-Supervised Object Detection (WSOD) and localization (WSOL) are long-standing and challenging CV tasks. With the success of deep neural networks in object detection, both WSOD and WSOL have received unprecedented attention. Hundreds of WSOD and WSOL methods and numerous techniques have been proposed in the deep learning era.
• Score: 37.545083067084306
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Weakly-Supervised Object Detection (WSOD) and Localization (WSOL), i.e., detecting multiple and single instances with bounding boxes in an image using image-level labels, are long-standing and challenging tasks in the CV community. With the success of deep neural networks in object detection, both WSOD and WSOL have received unprecedented attention. Hundreds of WSOD and WSOL methods and numerous techniques have been proposed in the deep learning era. To this end, in this paper, we consider WSOL is a sub-task of WSOD and provide a comprehensive survey of the recent achievements of WSOD. Specifically, we firstly describe the formulation and setting of the WSOD, including the background, challenges, basic framework. Meanwhile, we summarize and analyze all advanced techniques and training tricks for improving detection performance. Then, we introduce the widely-used datasets and evaluation metrics of WSOD. Lastly, we discuss the future directions of WSOD. We believe that these summaries can help pave a way for future research on WSOD and WSOL.

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