FeaEM:Feature Enhancement-based Method for Weakly Supervised Salient Object Detection via Multiple Pseudo Labels
Salient object detection is designed to detect the most obvious areas of an image.The traditional method based on single label is inevitably affected by the refinement algorithm and shows bias characteristics,which further affects the detection perfor-mance of saliency network.To solve this problem,based on the structure of multi-instruction filter,this paper proposes a feature enhancement-based method for weakly supervised salient object detection via multiple pseudo labels(FeaEM),which integrates more comprehensive and accurate saliency cues from multiple labels to effectively improve the performance of object detection.The core of FeaEM method is to introduce a new multi-instruction filter structure and use multiple pseudo-labels to avoid the negative effects of a single label.By introducing the feature selection mechanism into the instruction filter,more accurate signifi-cance clues are extracted and filtered from the noise false label,so as to learn more effective representative features.At the same time,the existing weak supervised object detection methods are very sensitive to the scale of the input image,and the prediction structure of the input of different sizes of the same image has a large deviation.The scale feature fusion mechanism is introduced to ensure that the output of the same image of different sizes is consistent,so as to effectively improve the scale generalization ability of the model.A large number of experiments on multiple data sets show that the FeaEM method proposed in this paper is superior to the most representative methods.
Deep learningObject detectionSalientPseudo labelsAttention mechanism
NETL
NSTL
万方数据
