Attention fine-grained classification method based on weak supervision position positioning

A classification method and attention technology, applied in the field of image classification, can solve the problems of time-consuming and laborious, difficult, unfavorable application, etc., and achieve the effect of reducing size, strengthening recognition ability, and considerable competitiveness.

Inactive Publication Date: 2022-06-07
ZHEJIANG GONGSHANG UNIVERSITY
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] 1. Manually defined parts may not be the best choice for the final classification task
[0005] Second, labeling parts is much more difficult than collecting image labels. Manually cropping objects and labeling their parts is time-consuming and laborious, which is not conducive to practical applications
[0006] 3. The proposal method for unsupervised object regions will generate a large number of proposals (some as many as thousands), and the cost of computing, processing and classifying candidate regions is very high.

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
  • Attention fine-grained classification method based on weak supervision position positioning
  • Attention fine-grained classification method based on weak supervision position positioning
  • Attention fine-grained classification method based on weak supervision position positioning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0054] like Figure 1 to Figure 3 As shown in the figure, a fine-grained classification method of attention based on weakly supervised position location, specifically includes the following steps:

[0055] 101) Preprocessing step: constructing an initial object detection model, and extracting position information (x, y, l), size and semantic score in the model to prepare for the subsequent visual attention model;

[0056] The specific implementation of preprocessing is as follows:

[0057] When building the initial object detection model, the data in the training phase needs to be augmented, and the data augmentation includes horizontal / vertical flipping, translation, and scale variance. The initial network is trained using the SGD optimizer. The initial learning rate is 0.001, following a polynomial decay strategy with a weight decay of 0.0001. The batch size in the experiments is 8.

[0058] The object detection model consists of ResNet-50. The input data of the model co...

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 an attention fine-grained classification method based on weak supervision position positioning. The method specifically comprises the following steps: 101) a preprocessing step, 102) an information processing step, 103) an information progress step, 104) a feature data step and 105) a classification confirmation step. The invention provides an attention fine-grained classification method based on weak supervision position positioning, which can be used for positioning areas with relatively small inter-class differences and carrying out positioning and amplification.

Description

technical field [0001] The invention relates to the technical field of image classification, and more particularly, to a fine-grained classification method of attention based on weakly supervised position location. Background technique [0002] Fine-grained classification has attracted a lot of attention from the multimedia and computer vision communities, and the classification goal aims to distinguish visually and semantically very similar categories in general categories, such as various birds, dogs, and different car categories. The fine-grained object classification task is particularly beneficial for multimedia information retrieval and content analysis. An important observational task for fine-grained classification is that objects often play an important role in distinguishing some local part of a sub-category. For example, a dog's head is the key to distinguishing multiple types of dogs. Now studies distinguish dog species by observing dog heads, most of the fine-...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08G06T7/70G06V10/764G06V10/82
CPCG06N3/084G06T7/70G06N3/044G06N3/045G06F18/241
Inventor 平佳锜邢建国
Owner ZHEJIANG GONGSHANG UNIVERSITY
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