Dilated convolution.

Dilated convolution.

<div><p>This study aims to explore a data-driven cultural background fusion method to improve the accuracy of environmental art image classification. A novel Dual Kernel Squeeze and Excitation Network (DKSE-Net) model is proposed for the complex cultural background and diverse visual rep...

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Bibliographic Details
Main Author: Chenchen Liu (503953) (author)
Other Authors: Haoyue Guo (3991031) (author)
Published: 2025
Subjects:
Biotechnology
Ecology
Space Science
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
receptive fields using
experimental results indicate
diverse visual representation
deep learning technology
7 %, 3
complex cultural background
selective kernel network
model &# 8217
complex cultural backgrounds
global average pooling
environmental art images
dual kernel squeeze
feature capture ability
layer convolution process
second layer convolution
higher classification accuracy
net model achieves
cultural backgrounds
initial layer
processing images
generalization ability
excitation network
classification accuracy
art models
feature maps
feature extraction
model combines
pointwise convolution
dilated convolution
depthwise convolution
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thus enhancing
technical support
study aims
senet ).
prevent overfitting
performance provides
local features
l2 regularization
important reference
existing state
comprehensively extract
comparative models
broad potential
batch normalization
adaptive adjustment
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