Cross-domain image example level active labeling method

Abstract

The invention discloses a cross-domain image example level active labeling method. Digital image target detection is one of the basic tasks of computer vision, and generally needs a large number of samples with object frame labels to be used for training a machine learning model. However, in real tasks, a large number of training samples of target tasks cannot be obtained due to sensibility and the like, so that the model performance is low, and the model is difficult to promote. According to the method, the unsupervised source domain which is easy to obtain and rich in knowledge is utilized, and efficient example labeling is automatically selected through an active learning technology, so that finer labeling information is obtained, and the data labeling difficulty is greatly reduced; meanwhile, the obtained supervision information is fully utilized, the performance of the model on the target task is efficiently improved, and the participation cost of the user can be remarkably reduced.

Description

technical field [0001] The invention belongs to the technical field of digital image automatic labeling, and in particular relates to a cross-domain image instance-level active labeling method. Background technique [0002] Semantic understanding of digital images is an important fundamental task in the field of artificial intelligence. Object detection is the key technology, which tries to predict the bounding box and its category of each object in the image. Existing detection models usually accomplish object detection tasks by decomposing images into a large number of regions of different sizes, i.e., examples, and making example-level predictions. The training of detection models often requires a large number of complete example-level labeled pictures, which is expensive. Automatic image annotation techniques are often widely used to reduce annotation costs. However, in some practical tasks, target domain data cannot be obtained in large quantities due to sensitive in...

AI Technical Summary

Problems solved by technology

[0003] Purpose of the invention: In order to overcome the difficulties in obtaining target domain data and improving model

Images