DSSESKN: A depthwise separable squeeze-and-excitation selective kernel network for art image classification
Image classification is one of the key technologies of content-based image retrieval, and it is also the focus and hotspot of image content analysis research in recent years. Through the image processing and analysis technology to automatically analyze the image content to complete the management a...
Main Author: | Shaojie Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
European Alliance for Innovation (EAI)
2021-11-01
|
Series: | EAI Endorsed Transactions on Scalable Information Systems |
Subjects: | |
Online Access: | https://publications.eai.eu/index.php/sis/article/view/308 |
Similar Items
-
Feature Recalibration in Deep Learning via Depthwise Squeeze and Refinement Operations
by: Xingpeng Zhang, et al.
Published: (2020-01-01) -
Depthwise Separable Relation Network for Small Sample Hyperspectral Image Classification
by: Aili Wang, et al.
Published: (2021-09-01) -
Multi-Scale Depthwise Separable Convolution for Semantic Segmentation in Street–Road Scenes
by: Yingpeng Dai, et al.
Published: (2023-05-01) -
Hybrid Convolutional Network Combining 3D Depthwise Separable Convolution and Receptive Field Control for Hyperspectral Image Classification
by: Chengle Lin, et al.
Published: (2022-12-01) -
Reduction of Feature Extraction for COVID-19 CXR using Depthwise Separable Convolution Network
by: Zendi Iklima, et al.
Published: (2022-10-01)