Small sample target detection method based on attention and contrast learning

Abstract

The invention relates to a small sample target detection method based on attention and contrast learning, and belongs to the field of artificial intelligence and image processing. The invention relates to the small sample target detection method which combines data enhancement, an attention region suggestion network (Attention RPN) and comparative learning. The method is based on a Faster R-CNN (Convolutional Neural Network) network, and comprises the following steps of: adopting a Few-shot Mosaic data enhancement module for enriching the comparison between a small sample background instance and a Novelclass instance and a Base class instance, enhancing the attention of a model on a foreground by an Attention RPN (Regression Coordinate Compensation) module based on regression coordinate compensation, and improving the expression of instance-level features by a contrast learning module. According to the method, the new class detection precision of the Faster R-CNN on a small sample is improved, and meanwhile, relatively high base class detection precision is kept; the dependency of Faster R-CNN on the new class training sample size is reduced, the new class migration ability is improved, and the effectiveness of the method is verified on COCO and VOC data sets.

Description

technical field [0001] The invention relates to a small-sample target detection method, specifically designs a small-sample target detection method based on attention and contrastive learning, and belongs to the fields of artificial intelligence and image processing. Background technique [0002] Target detection is to detect the target and calibrate the target position in the picture at the same time, so the task of target detection is more complicated than that of picture classification, involving not only classification tasks but also regression tasks. Since the data samples have long-tail distribution characteristics on the one hand, and some samples are difficult to obtain on the other hand, the cost of labeling is huge. Therefore, there is also a small sample problem in target detection, that is, after training on a base class sample with a large sample size, fine-tuning training is performed on a new class with a small sample size and a small sample size, so that the ...

AI Technical Summary

Problems solved by technology

[0009] The meta-learning method is actually an attention mechanism, which is based on the attention of the support set and the query set, but it does not make full use of the relationship between instances. The attention pays more attention to the differences between different instances. Contrastive learning can pay attention to both differences and common

The fine-tuning method will have different parameter settings depending on the data set. It is not an end-to-end self-learning model. Although it is feasible empirically, it does not solve the fundamental problem, that is, forgetting when knowledge transfers.

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