关键词: 卷积神经网络, 梯度金字塔, 弱监督定位

Abstract: Aiming at solving the problems of missing and non-locating labels in many datasets in practical applications, a weakly supervised positioning algorithm based on multi-scale feature convolutional neural network is proposed. The core idea uses the characteristics of neural network to generate gradient pyramid models by using gradient weighted class activation mapping on multi-layer convolutional layers. Besides, the feature centroid position is calculated by mean filtering operation, and the connected pixel segments are generated by the confidence intensity map and the threshold clipping module. The weakly supervised positioning is performed around the maximum boundary label. The experimental results on the standard benchmark show that the proposed algorithm can achieve target positioning on datasets with high accuracy which have a large number of categories and multi-scale images.

Key words: convolutional neural network, gradient pyramid, weakly supervised positioning