Semantic segmentation method under small sample based on variational prototype reasoning

A semantic segmentation, small sample technology, applied in the fields of instruments, character and pattern recognition, computer parts, etc., can solve problems such as deviation, difficulty in achieving results, and lack of generalization ability.

Pending Publication Date: 2020-09-22
GUANGDONG UNIV OF PETROCHEMICAL TECH
View PDF3 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this method is difficult to achieve good results in the case of small samples. In the case of sm...

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Semantic segmentation method under small sample based on variational prototype reasoning
  • Semantic segmentation method under small sample based on variational prototype reasoning
  • Semantic segmentation method under small sample based on variational prototype reasoning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0063] see figure 1 , a small-sample semantic segmentation method based on variational prototype reasoning, including the following steps:

[0064] Input: known image x to be segmented q and the labeled support set image S, as well as the prior network parameters θ and segmentation network parameters ψ obtained through the segmentation learning process;

[0065] Output: Segmented Image Map

[0066] S1. According to the prior probability network, the mean value and variance corresponding to the support set image S are generated as follows:

[0067]

[0068] S2. Calculate the implicit representation of the space z of the prior probability network map:

[0069] z←μ prior +∈⊙σ prior , ∈~N(0,1);

[0070] S3, perform multiple sampling on z in S2 to generate z (l) ;

[0071] S4, each z (l) and x q Send it to the segmentation network and generate as follows as follows:

[0072]

[0073] Three networks are involved in the segmentation learning process: prior networ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a semantic segmentation method under a small sample based on variational prototype reasoning, and belongs to the field of computer vision semantic segmentation. The invention discloses a semantic segmentation method under a small sample based on variational prototype reasoning. According to the invention, variational prototype reasoning is proposed for the first time; semantic segmentation under a small sample is brought into a probability framework; in the probability framework, prototype representation is not vector representation of a fixed numerical value, but distribution, distribution of a hidden space in variational reasoning is used for representing distribution of the prototype, and the generalization ability of the whole prototype is improved under the condition of a small sample so as to adapt to uncertainty represented by the small sample; moreover, an objective function of variational prototype reasoning suitable for image semantic segmentation under a probability framework is proposed for the first time, a semantic segmentation process under a small sample is assisted, and multiple tests prove that a very good segmentation effect is also obtained under the condition of utilizing a single support set image.

Description

technical field [0001] The invention relates to the field of computer vision semantic segmentation, and more specifically, relates to a semantic segmentation method under small samples based on variational prototype reasoning. Background technique [0002] Deep learning has been widely used in semantic segmentation of computer vision, but due to the lack of labeled supporting data in practical semantic segmentation applications, the performance of the learned deep model is affected. At present, the prototype-based method is more popular. Here, the prototype refers to the representation of a class of objects. In the deep learning framework, it is the output generated by a deep neural network based on the label information of the supporting image and its corresponding object. In other words, a prototype is an associative map between input support images and object categories. [0003] For semantic segmentation under small samples, it is basically based on the prototype method...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/34G06K9/62
CPCG06V10/267G06F18/29G06F18/214Y02D10/00
Inventor 甄先通张磊李欣左利云简治平陈林凯胥亮李晓莹
Owner GUANGDONG UNIV OF PETROCHEMICAL TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap