Zobrazeno 1 - 10
of 347
pro vyhledávání: '"Chen, Keyan"'
Segmenting anatomical structures and lesions from ultrasound images contributes to disease assessment, diagnosis, and treatment. Weakly supervised learning (WSL) based on sparse annotation has achieved encouraging performance and demonstrated the pot
Externí odkaz:
http://arxiv.org/abs/2409.19370
Autor:
Li, Kaiyu, Jiang, Jiawei, Codegoni, Andrea, Han, Chengxi, Deng, Yupeng, Chen, Keyan, Zheng, Zhuo, Chen, Hao, Zou, Zhengxia, Shi, Zhenwei, Fang, Sheng, Meng, Deyu, Wang, Zhi, Cao, Xiangyong
We present Open-CD, a change detection toolbox that contains a rich set of change detection methods as well as related components and modules. The toolbox started from a series of open source general vision task tools, including OpenMMLab Toolkits, P
Externí odkaz:
http://arxiv.org/abs/2407.15317
Remote sensing image change captioning (RSICC) aims to articulate the changes in objects of interest within bi-temporal remote sensing images using natural language. Given the limitations of current RSICC methods in expressing general features across
Externí odkaz:
http://arxiv.org/abs/2407.14032
Recently, the Mamba architecture based on state space models has demonstrated remarkable performance in a series of natural language processing tasks and has been rapidly applied to remote sensing change detection (CD) tasks. However, most methods en
Externí odkaz:
http://arxiv.org/abs/2406.04207
Remote Sensing Image Change Captioning (RSICC) aims to describe surface changes between multi-temporal remote sensing images in language, including the changed object categories, locations, and dynamics of changing objects (e.g., added or disappeared
Externí odkaz:
http://arxiv.org/abs/2404.18895
The segmentation and interpretation of the Martian surface play a pivotal role in Mars exploration, providing essential data for the trajectory planning and obstacle avoidance of rovers. However, the complex topography, similar surface features, and
Externí odkaz:
http://arxiv.org/abs/2404.04155
Remote sensing image classification forms the foundation of various understanding tasks, serving a crucial function in remote sensing image interpretation. The recent advancements of Convolutional Neural Networks (CNNs) and Transformers have markedly
Externí odkaz:
http://arxiv.org/abs/2403.19654
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing 2024
Monitoring changes in the Earth's surface is crucial for understanding natural processes and human impacts, necessitating precise and comprehensive interpretation methodologies. Remote sensing satellite imagery offers a unique perspective for monitor
Externí odkaz:
http://arxiv.org/abs/2403.19646
Autor:
Liu, Zili, Chen, Hao, Bai, Lei, Li, Wenyuan, Chen, Keyan, Wang, Zhengyi, Ouyang, Wanli, Zou, Zhengxia, Shi, Zhenwei
Downscaling (DS) of meteorological variables involves obtaining high-resolution states from low-resolution meteorological fields and is an important task in weather forecasting. Previous methods based on deep learning treat downscaling as a super-res
Externí odkaz:
http://arxiv.org/abs/2401.11960
Accurate weather forecasting holds significant importance to human activities. Currently, there are two paradigms for weather forecasting: Numerical Weather Prediction (NWP) and Deep Learning-based Prediction (DLP). NWP utilizes atmospheric physics f
Externí odkaz:
http://arxiv.org/abs/2401.04125