Abstract：The bounding box with semantic information is used as the weak supervised annotations,and the object bounding box is used as a priori clue to find the feature points that belong to the target object but have weak activation value in the classification network. The correlation of the neuron nodes between the convolution layers is found by probability back propagation mechanism, and a complete class attention map of the object is obtained. In addition, by combining the image super-pixel algorithm, the rough dividing effect at the edge of the attention map is improved by the filling rate selection strategy, and the optimal category mask is generated. The extensive experiment results show that the method proposed method improves the integrity of the positioning of attention mechanism, and obtains 64.8% mIoU score results on the Pascal VOC2012 segmentation dataset.
李良御. 基于类别概率反向传播机制的弱监督语义分割[J]. 电气技术, 2020, 21(4): 80-84.
Li Liangyu. Weakly supervised semantic segmentation based on category probability back propagation mechanism. Electrical Engineering, 2020, 21(4): 80-84.
2020, Vol. 21 
