1 code implementation • 20 Mar 2024 • Linshan Wu, Zhun Zhong, Jiayi Ma, Yunchao Wei, Hao Chen, Leyuan Fang, Shutao Li
Based on the label distributions, we leverage the GMM to generate high-quality pseudo labels for more reliable supervision.
Weakly supervised Semantic Segmentation Weakly-Supervised Semantic Segmentation
1 code implementation • 9 Jan 2024 • Linshan Wu, Ming Lu, Leyuan Fang
Compared with the existing category alignment methods, our CR aims to regularize the correlation between different dimensions of the features and thus performs more robustly when dealing with the divergent category features of imbalanced and inconsistent distributions.
no code implementations • 11 Dec 2023 • Shaobo Xia, Jun Yue, Kacper Kania, Leyuan Fang, Andrea Tagliasacchi, Kwang Moo Yi, Weiwei Sun
We propose a weakly supervised semantic segmentation method for point clouds that predicts "per-point" labels from just "whole-scene" annotations while achieving the performance of recent fully supervised approaches.
1 code implementation • 1 Dec 2023 • Weiying Xie, Xiaoyi Fan, Xin Zhang, Yunsong Li, Jie Lei, Leyuan Fang
Pruning-quantization joint learning always facilitates the deployment of deep neural networks (DNNs) on resource-constrained edge devices.
1 code implementation • 16 Nov 2023 • Daixun Li, Weiying Xie, Yunsong Li, Leyuan Fang
Multi-satellite, multi-modality in-orbit fusion is a challenging task as it explores the fusion representation of complex high-dimensional data under limited computational resources.
no code implementations • 16 Nov 2023 • Daixun Li, Weiying Xie, Zixuan Wang, YiBing Lu, Yunsong Li, Leyuan Fang
With the rapid development of imaging sensor technology in the field of remote sensing, multi-modal remote sensing data fusion has emerged as a crucial research direction for land cover classification tasks.
1 code implementation • 19 Jul 2023 • Jitao Ma, Weiying Xie, Yunsong Li, Leyuan Fang
We present a novel solution BSDM (background suppression diffusion model) for HAD, which can simultaneously learn latent background distributions and generalize to different datasets for suppressing complex background.
1 code implementation • 12 Apr 2023 • Ning Chen, Jun Yue, Leyuan Fang, Shaobo Xia
The framework consists of a spectral-spatial diffusion module, and an attention-based classification module.
no code implementations • 19 Jan 2023 • Jun Yue, Leyuan Fang, Shaobo Xia, Yue Deng, Jiayi Ma
In specific, instead of converting multi-channel images into single-channel data in existing fusion methods, we create the multi-channel data distribution with a denoising network in a latent space with forward and reverse diffusion process.
1 code implementation • CVPR 2023 • Weiying Xie, Kai Jiang, Yunsong Li, Jie Lei, Leyuan Fang, Wen-jin Guo
Specifically, we create a positive cycle between fusion and degradation estimation under a new probabilistic framework.
1 code implementation • CVPR 2023 • Yifan Lu, Jiayi Ma, Leyuan Fang, Xin Tian, Junjun Jiang
This enables the application of Gaussian processes to a wide range of real data, which are often large-scale and contaminated by outliers.
1 code implementation • CVPR 2023 • Linshan Wu, Zhun Zhong, Leyuan Fang, Xingxin He, Qiang Liu, Jiayi Ma, Hao Chen
Our AGMM can effectively endow reliable supervision for unlabeled pixels based on the distributions of labeled and unlabeled pixels.
no code implementations • 18 Apr 2022 • Jun Yue, Leyuan Fang, Pedram Ghamisi, Weiying Xie, Jun Li, Jocelyn Chanussot, Antonio J Plaza
Therefore, remote sensing image understanding often faces the problems of incomplete, inexact, and inaccurate supervised information, which will affect the breadth and depth of remote sensing applications.
no code implementations • 11 Mar 2022 • YaoWei Wang, Zhouxin Yang, Rui Liu, Deng Li, Yuandu Lai, Leyuan Fang, Yahong Han
Considering the diversity and complexity of scenes in intelligent city governance, we build a large-scale object detection benchmark for the smart city.
no code implementations • 10 Jan 2022 • Ming Lu, Leyuan Fang, Muxing Li, Bob Zhang, Yi Zhang, Pedram Ghamisi
Therefore, we study how to utilize point labels to extract water bodies and propose a novel method called the neighbor feature aggregation network (NFANet).
no code implementations • 24 Nov 2021 • Jiahui Ni, Zhimin Shao, Zhongzhou Zhang, Mingzheng Hou, Jiliu Zhou, Leyuan Fang, Yi Zhang
In addition, a novel hybrid loss function is proposed to constrain both spatial and spectral consistency between the pansharpened image and the PAN and LRMS images at different resolutions.
2 code implementations • 27 Oct 2021 • Runmin Cong, Yumo Zhang, Leyuan Fang, Jun Li, Yao Zhao, Sam Kwong
Salient object detection (SOD) for optical remote sensing images (RSIs) aims at locating and extracting visually distinctive objects/regions from the optical RSIs.
1 code implementation • 25 Mar 2020 • Ashkan Abbasi, Amirhassan Monadjemi, Leyuan Fang, Hossein Rabbani, Neda Noormohammadi, Yi Zhang
The data-driven sparse methods such as synthesis dictionary learning (e. g., K-SVD) and sparsifying transform learning have been proven effective in image denoising.
no code implementations • 26 Oct 2019 • Shutao Li, Weiwei Song, Leyuan Fang, Yushi Chen, Pedram Ghamisi, Jón Atli Benediktsson
Specifically, we first summarize the main challenges of HSI classification which cannot be effectively overcome by traditional machine learning methods, and also introduce the advantages of deep learning to handle these problems.
no code implementations • 22 Nov 2018 • Ashkan Abbasi, Amirhassan Monadjemi, Leyuan Fang, Hossein Rabbani, Yi Zhang
In recent years, there has been a growing interest in applying convolutional neural networks (CNNs) to low-level vision tasks such as denoising and super-resolution.
no code implementations • CVPR 2017 • Renwei Dian, Leyuan Fang, Shutao Li
In this paper, a novel HSI super-resolution method based on non-local sparse tensor factorization (called as the NLSTF) is proposed.