Research On Weakly Supervised Semantic Segmentation Algorithm Based On Image-level Label

Semantic segmentation is an important branch of computer vision tasks,which can be considered as the underlying pixel classification problem.Thanks to the development of deep learning theory and the improvement of computers’ parallel computing power,the accuracy of current semantic segmentation algorithms has been greatly improved.However,contemporary semantic segmentation algorithms use a fully supervised training model,which means that pixel-level image labels are used to train the network,and it is obvious that such labeling requires a lot of human and financial resources,which hinders the application scenarios of semantic segmentation.In order to reduce the dependence on pixel labels,some scholars have proposed weakly supervised semantic segmentation algorithms,which eschew traditional pixel-level image labels in favor of more easily obtained and labeled image labels,such as image-level labels,bounding box-level labels,and scribblelevel labels,among which image-level labels are the easiest to label,and thus many weakly supervised semantic segmentation algorithms use such labels.Since image-level labels only indicate the presence or absence of the target object in the image,losing semantic information that is crucial for semantic segmentation,the emergence of class activation maps(CAM)provides crucial a priori information about the target location for weakly supervised semantic segmentation.But the class activation maps only determine small discriminative regions,which is not sufficient for semantic segmentation.In response to this problem,the following work is carried out in this paper.(1)A multi-feature fusion module(MFFM)is designed to make full use of the feature maps obtained at each stage to compensate for the loss of semantic information at a shallow level,and combine the use of cascaded and parallel dilated convolution operations to expand the perceptual field of the feature maps.To obtain the relationship between surrounding regions and adjacent and distant regions,a global reasoning unit is further introduced to calculate the relationship between surrounding pixels.(2)The attention hybrid pooling module(AHPM)is designed to replace the traditional global average pooling(GAP)operation to solve the misclassification problem of background pixels,and the widely used spatial attention mechanism and channel attention mechanism are utilized to rearrange weights for feature maps.(3)A global reasoning network is designed by combining MFFM and AHPM,and training the network on the PASCAL VOC 2012 datasets obtains class activation maps that include a larger range of targets,rather than being limited to only small salient target regions.In order to obtain better segmentation performance,the paper designs siamese network,and the experimental results verify the superiority of the siamese network.