Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Method for generating a web page screenshot code based on a neural network and a self-attention mechanism

A code generation and neural network technology, applied in the field of code generation of web page screenshots based on neural network and self-attention mechanism, can solve the problems of low technical requirements and time-consuming, achieve high practical value and improve development efficiency

Inactive Publication Date: 2019-03-26
SUN YAT SEN UNIV
View PDF4 Cites 13 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

For different platforms, such as Android, IOS or webpages, the front-end webpage codes are generated by HTML language. The generation of these webpages usually has low technical requirements but is very time-consuming

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
  • Method for generating a web page screenshot code based on a neural network and a self-attention mechanism

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0024] A code generation method for webpage screenshots based on neural network and self-attention mechanism, please refer to figure 1 , including the following steps:

[0025] S1: Use convolutional neural network to extract the key features of webpage screenshots of several platforms; use convolutional neural network to extract features for webpage screenshots, specifically, extract obvious features in screenshots by using cyclic neural network, Such as navigation bars, buttons, and text boxes, generate their corresponding vectors.

[0026] S2: Through the application of the self-attention mechanism, the key features obtained in step S1 and the webpage code are associated with learning to improve the attention of the text to its own information; the application of the self-attention mechanism specifically includes assuming that the features of the webpage screenshots are extracted and expressed as vectors [ a 1 , a 2 ,...,a n ], and get the inner product between any two v...

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 method for generating a web page screenshot code based on a neural network and a self-attention mechanism, which comprises the following steps: extracting key features from web page screenshots of several platforms through a convolution neural network; generating a web page screenshot code based on the web page screenshots of several platforms through a convolution neuralnetwork. By applying the self-attention mechanism, carrying out associated learning operation on the key features obtained in step S1 and the web page code to enhance the attention of the text to itsown information; learning The processed image features and web page codes through the loop neural network, and acquiring the corresponding network parameters. Generating a pseudo code of a web page screenshot using the network parameters obtained in step S4; compiling The pseudo code in step S4 further to obtain the final generated web screenshot code. The invention can better identify the features in the web page screenshot, can better pay attention to the syntax associated with the front and back in the code text and match with the generated web page screenshot features, has high practicalvalue, and can help the developer of the front section to improve the development efficiency.

Description

technical field [0001] The present invention relates to the field of image processing, natural language processing and front-end code generation technology, and more specifically, to a code generation method for web page screenshots based on neural network and self-attention mechanism. Background technique [0002] Deep learning models have achieved impressive results in computer vision, speech recognition, and natural language processing. And these algorithms and technologies can be used in many aspects of life and production, such as automatically generating web page screenshot codes. For different platforms, such as Android, IOS or webpages, the front-end webpage codes are generated by HTML language. The generation of these webpages usually requires very low technical requirements but is very time-consuming. Therefore, applying the latest technology deep learning model to this requirement can greatly reduce the development pressure of developers and improve development e...

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): G06F8/38G06F8/41G06K9/62G06N3/04G06N3/08
CPCG06F8/38G06F8/447G06N3/08G06N3/044G06N3/045G06F18/24
Inventor 景涛林瀚
Owner SUN YAT SEN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com