no code implementations • 14 Nov 2023 • Dalong Zheng, Zebin Wu, Jia Liu, Chih-Cheng Hung, Zhihui Wei
In order to fully mine these three kinds of change features, we propose the triple branch network combining the transformer and convolutional neural network (CNN) to extract and fuse these change features from two perspectives of global information and local information, respectively.
no code implementations • 22 Aug 2023 • Dalong Zheng, Zebin Wu, Jia Liu, Zhihui Wei
Therefore, based on swin transformer V2 (Swin V2) and VGG16, we propose an end-to-end compounded dense network SwinV2DNet to inherit the advantages of both transformer and CNN and overcome the shortcomings of existing networks in feature learning.
no code implementations • 17 Oct 2022 • Chongyu Sun, Yang Xu, Zebin Wu, Zhihui Wei
This paper proposes a Rotation-equivariant Attention Feature Fusion Pyramid Networks for Aerial Object Detection named ReAFFPN.
no code implementations • 13 Jun 2021 • Huapeng Wu, Jie Gui, Jun Zhang, James T. Kwok, Zhihui Wei
Recently, deep convolutional neural network methods have achieved an excellent performance in image superresolution (SR), but they can not be easily applied to embedded devices due to large memory cost.
no code implementations • 13 Jun 2021 • Huapeng Wu, Jie Gui, Jun Zhang, James T. Kwok, Zhihui Wei
Recently, convolutional neural network (CNN) based image super-resolution (SR) methods have achieved significant performance improvement.
no code implementations • 26 Sep 2019 • Huapeng Wu, Zhengxia Zou, Jie Gui, Wen-Jun Zeng, Jieping Ye, Jun Zhang, Hongyi Liu, Zhihui Wei
In this paper, we make a thorough investigation on the attention mechanisms in a SR model and shed light on how simple and effective improvements on these ideas improve the state-of-the-arts.