关键词: 弱监督学习, 目标检测, 多示例学习, 类激活图, 注意力机制, 伪标签

Abstract:

With the continuous development of Convolutional Neural Network（CNN）, as the most basic technology in computer vision, object detection has made remarkable progress. Firstly, the current situation that the strong supervised object detection algorithm requires high precision for labeling datasets is introduced. Secondly, the object detection algorithm based on weakly supervised learning is studied. The algorithm is classified into four categories according to different feature processing methods, and the advantages and disadvantages of each algorithm are analyzed and compared. Thirdly, the detection accuracy of all kinds of object detection algorithms based on weakly supervised learning is compared through experiments. At the same time, it is compared with the mainstream strong supervised object detection algorithms. Finally, the future research hotspots of object detection algorithms based on weakly supervised learning are prospected.

Key words: weakly supervised learning, object detection, multi-instance learning, class activation map, attention mechanism, pseudo label