Search Results for author: Hao Chang

Found 12 papers, 6 papers with code

RS-vHeat: Heat Conduction Guided Efficient Remote Sensing Foundation Model

no code implementations27 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.

Computational Efficiency

Approaching Maximum Likelihood Performance via End-to-End Learning in MU-MIMO Systems

no code implementations25 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.

Graph-based Untrained Neural Network Detector for OTFS Systems

no code implementations8 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.

Decoder

See SIFT in a Rain

1 code implementation1 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.

Rain Removal

Untrained Neural Network based Bayesian Detector for OTFS Modulation Systems

no code implementations8 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.

Decoder

Pseudo-Label Generation and Various Data Augmentation for Semi-Supervised Hyperspectral Object Detection

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.

Data Augmentation object-detection +3

A viable framework for semi-supervised learning on realistic dataset

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.

Scene Clustering Based Pseudo-labeling Strategy for Multi-modal Aerial View Object Classification

2 code implementations4 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.

Clustering Image Classification

BiSTF: Bilateral-Branch Self-Training Framework for Semi-Supervised Large-scale Fine-Grained Recognition

no code implementations14 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.

Skin cancer reorganization and classification with deep neural network

no code implementations1 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.

Boundary Detection Classification +6

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