A Framework for Improving the Effect of Semantic Segmentation Models Based on Transfer Learning

Abstract

The invention discloses a framework for improving the effect of a semantic segmentation model based on transfer learning, including the following content: 1) introducing transfer learning into the field of semantic segmentation, so that the fast semantic segmentation network can improve the segmentation effect of the student model through the teacher model; 2) A consistency map is proposed to measure the contour and texture information of the teacher and student models, and the fast semantic segmentation can be segmented better at the details by constructing a consistency loss function; 3) Using the teacher model and the conditional random field (CRF) model as an The label data generates auxiliary labels, and the data is added to the training set to improve the generalization ability and segmentation effect of the model. The present invention improves the accuracy of the fast semantic segmentation model without introducing additional model parameters and reducing the speed of the model.

Description

technical field [0001] The invention belongs to the technical field of semantic segmentation, and in particular relates to a framework for improving the effect of a semantic segmentation model based on migration learning. Background technique [0002] Image semantic segmentation is a basic and important research in the field of computer vision, which requires the model to identify which semantic category each pixel in the image belongs to. Image semantic segmentation has many applications, such as automatic driving and auxiliary robots. Image semantic segmentation is also the basis of video semantic segmentation. By treating video frames as a single image, the problem can be transformed into semantic segmentation of images, which can be further modeled in the temporal dimension. [0003] The existing work can be mainly divided into two categories: accuracy-oriented semantic segmentation and speed-oriented semantic segmentation. The precision-oriented semantic segmentation ...

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Problems solved by technology

Compared with the first) method, the second) method is much worse in segmentation accuracy, which is also the main shortcoming of the fast semantic segmentation network

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