Weak supervision target detection method based on image attribute learning
A target detection, weakly supervised technology, applied in neural learning methods, instruments, biological neural network models, etc.
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[0071] The present invention will now be further described in conjunction with the embodiments and accompanying drawings:
[0072] This method is based on a weakly supervised target detection method represented by image attributes. The model used consists of five sub-modules: image feature extraction and target proposal frame extraction, text processing and feature extraction module, pseudo ground-truth mining module, image Attribute learning and classification module, target classification and detection box regression module.
[0073] 1. Image feature extraction and target proposal box extraction module
[0074] First, use the convolutional neural network module (VGG16 or ResNet) to extract features from the original image. The result of extraction is a feature vector on the entire image, and then this feature vector is used as an input to the RPN (Region Proposal Network) network, and the target proposal frame on the entire image can be finally extracted through the RPN net...
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