Search Results for author: Wenyuan Li

Found 16 papers, 7 papers with code

Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method

no code implementations22 Jan 2024 Zili Liu, Hao Chen, Lei Bai, Wenyuan Li, Keyan Chen, Zhengyi Wang, Wanli Ouyang, Zhengxia Zou, Zhenwei Shi

In this paper, we extend meteorological downscaling to arbitrary scattered station scales, establish a brand new benchmark and dataset, and retrieve meteorological states at any given station location from a coarse-resolution meteorological field.

Super-Resolution Weather Forecasting

Learning to detect cloud and snow in remote sensing images from noisy labels

no code implementations17 Jan 2024 Zili Liu, Hao Chen, Wenyuan Li, Keyan Chen, Zipeng Qi, Chenyang Liu, Zhengxia Zou, Zhenwei Shi

This paper is the first to consider the impact of label noise on the detection of clouds and snow in remote sensing images.

Semantic Segmentation

DeepPhysiNet: Bridging Deep Learning and Atmospheric Physics for Accurate and Continuous Weather Modeling

1 code implementation4 Jan 2024 Wenyuan Li, Zili Liu, Keyan Chen, Hao Chen, Shunlin Liang, Zhengxia Zou, Zhenwei Shi

Next, we construct hyper-networks based on deep learning methods to directly learn weather patterns from a large amount of meteorological data.

Weather Forecasting

RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model

1 code implementation28 Jun 2023 Keyan Chen, Chenyang Liu, Hao Chen, Haotian Zhang, Wenyuan Li, Zhengxia Zou, Zhenwei Shi

We also propose several ongoing derivatives for instance segmentation tasks, drawing on recent advancements within the SAM community, and compare their performance with RSPrompter.

Image Segmentation Instance Segmentation +2

Continuous Remote Sensing Image Super-Resolution based on Context Interaction in Implicit Function Space

1 code implementation16 Feb 2023 Keyan Chen, Wenyuan Li, Sen Lei, Jianqi Chen, XiaoLong Jiang, Zhengxia Zou, Zhenwei Shi

Despite its fruitful applications in remote sensing, image super-resolution is troublesome to train and deploy as it handles different resolution magnifications with separate models.

Image Super-Resolution

Semantic-aware Dense Representation Learning for Remote Sensing Image Change Detection

1 code implementation27 May 2022 Hao Chen, Wenyuan Li, Song Chen, Zhenwei Shi

To achieve this, we obtain multiple points via class-balanced sampling on the overlapped area between views using the semantic mask.

Change Detection Representation Learning +2

Geographical Knowledge-driven Representation Learning for Remote Sensing Images

1 code implementation12 Jul 2021 Wenyuan Li, Keyan Chen, Hao Chen, Zhenwei Shi

The proliferation of remote sensing satellites has resulted in a massive amount of remote sensing images.

object-detection Object Detection +3

A Multi-resolution Model for Histopathology Image Classification and Localization with Multiple Instance Learning

no code implementations5 Nov 2020 Jiayun Li, Wenyuan Li, Anthony Sisk, Huihui Ye, W. Dean Wallace, William Speier, Corey W. Arnold

Large numbers of histopathological images have been digitized into high resolution whole slide images, opening opportunities in developing computational image analysis tools to reduce pathologists' workload and potentially improve inter- and intra- observer agreement.

General Classification Image Classification +2

A Social Search Model for Large Scale Social Networks

no code implementations9 May 2020 Yunzhong He, Wenyuan Li, Liang-Wei Chen, Gabriel Forgues, Xunlong Gui, Sui Liang, Bo Hou

Rather, content is generated and shared among users and organized around their social relations on social networks.

Information Retrieval Retrieval

An attention-based multi-resolution model for prostate whole slide imageclassification and localization

no code implementations30 May 2019 Jiayun Li, Wenyuan Li, Arkadiusz Gertych, Beatrice S. Knudsen, William Speier, Corey W. Arnold

The model achieved state-of-the-art performance for prostate cancer grading with an accuracy of 85. 11\% for classifying benign, low-grade (Gleason grade 3+3 or 3+4), and high-grade (Gleason grade 4+3 or higher) slides on an independent test set.

General Classification Multiple Instance Learning

Semi-supervised Rare Disease Detection Using Generative Adversarial Network

no code implementations3 Dec 2018 Wenyuan Li, Yunlong Wang, Yong Cai, Corey Arnold, Emily Zhao, Yilian Yuan

Rare diseases affect a relatively small number of people, which limits investment in research for treatments and cures.

Generative Adversarial Network

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