Improved Image Classification With Token Fusion
In this paper, we propose a method to improve image classification performance using the fusion of CNN and transformer structure. In the case of CNN, information about a local area on an image can be extracted well, but global information extraction is limited. On the other hand, the transformer has...
Main Authors: | Keong-Hun Choi, Jin-Woo Kim, Yao Wang, Jong-Eun Ha |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2023-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10171338/ |
Similar Items
-
Convolution-Transformer Adaptive Fusion Network for Hyperspectral Image Classification
by: Jiaju Li, et al.
Published: (2022-12-01) -
MDvT: introducing mobile three-dimensional convolution to a vision transformer for hyperspectral image classification
by: Xinyao Zhou, et al.
Published: (2023-12-01) -
Review of Image Classification Algorithms Based on Convolutional Neural Networks
by: Leiyu Chen, et al.
Published: (2021-11-01) -
Improved deep learning image classification algorithm based on Swin Transformer V2
by: Jiangshu Wei, et al.
Published: (2023-10-01) -
Multi-Level Feature Extraction Networks for Hyperspectral Image Classification
by: Shaoyi Fang, et al.
Published: (2024-02-01)