no code implementations • 27 Nov 2024 • Huiyang Hu, Peijin Wang, Hanbo Bi, Boyuan Tong, Zhaozhi Wang, Wenhui Diao, Hao Chang, Yingchao Feng, Ziqi Zhang, Qixiang Ye, Kun fu, Xian Sun
Remote sensing foundation models largely break away from the traditional paradigm of designing task-specific models, offering greater scalability across multiple tasks.
no code implementations • 25 Nov 2024 • Hao Chang, Hoang Triet Vo, Alva Kosasih, Branka Vucetic, Wibowo Hardjawana
Multi-user multiple-input multiple-output (MU-MIMO) systems allow multiple users to share the same wireless spectrum.
no code implementations • 8 Apr 2024 • Hao Chang, Branka Vucetic, Wibowo Hardjawana
Inter-carrier interference (ICI) caused by mobile reflectors significantly degrades the conventional orthogonal frequency division multiplexing (OFDM) performance in high-mobility environments.
1 code implementation • 1 Nov 2023 • Wei Wu, Hao Chang, Zhu Li
One is difference of Gaussian (DoG) pyramid recovery network (DPRNet) for SIFT detection, and the other gradients of Gaussian images recovery network (GGIRNet) for SIFT description.
no code implementations • 8 May 2023 • Hao Chang, Alva Kosasih, Wibowo Hardjawana, Xinwei Qu, Branka Vucetic
In this paper, we propose an untrained DNN based on the deep image prior (DIP) and decoder architecture, referred to as D-DIP that replaces the MMSE denoiser in the iterative detector.
1 code implementation • Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2022 • Jun Yu, Liwen Zhang, Shenshen Du, Hao Chang, Keda Lu, Zhong Zhang, Ye Yu, Lei Wang, Qiang Ling
To overcome these difficulties, this paper first select fewer but suitable data augmentation methods to improve the accuracy of the supervised model based on the labeled training set, which is suitable for the characteristics of hyperspectral images.
1 code implementation • Conference and Labs of the Evaluation Forum 2022 • Jun Yu, Hao Chang, Keda Lu, Guochen Xie, Liwen Zhang, Zhongpeng Cai, Shenshen Du, Zhihong Wei, Zepeng Liu, Fang Gao, Feng Shuang
This motivates us to explore the impact of different methods and components in fine-grained classification on FungiCLEF 2022.
2 code implementations • Machine Learning 2022 • Hao Chang, Guochen Xie, Jun Yu, Qiang Ling, Fang Gao, Ye Yu
Semi-supervised Fine-Grained Recognition is a challenging task due to the difficulty of data imbalance, high inter-class similarity and domain mismatch.
2 code implementations • 4 May 2022 • Jun Yu, Hao Chang, Keda Lu, Liwen Zhang, Shenshen Du, Zhong Zhang
Multi-modal aerial view object classification (MAVOC) in Automatic target recognition (ATR), although an important and challenging problem, has been under studied.
1 code implementation • Association for the Advancement of Artificial Intelligence 2021 • Jun Yu, Hao Chang, Keda Lu
It’s more efficient to look for ways improving the data based a fixed neural network architecture.
no code implementations • 14 Jul 2021 • Hao Chang, Guochen Xie, Jun Yu, Qiang Ling
Semi-supervised Fine-Grained Recognition is a challenge task due to the difficulty of data imbalance, high inter-class similarity and domain mismatch.
no code implementations • 1 Mar 2017 • Hao Chang
Here we report a novel strategy based on deep learning technique, and achieve very high skin lesion segmentation and melanoma diagnosis accuracy: 1) we build a segmentation neural network (skin_segnn), which achieved very high lesion boundary detection accuracy; 2) We build another very deep neural network based on Google inception v3 network (skin_recnn) and its well-trained weight.