Zobrazeno 1 - 10
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pro vyhledávání: '"Li, Weijia"'
We present Ksformer, utilizing Multi-scale Key-select Routing Attention (MKRA) for intelligent selection of key areas through multi-channel, multi-scale windows with a top-k operator, and Lightweight Frequency Processing Module (LFPM) to enhance high
Externí odkaz:
http://arxiv.org/abs/2406.19703
For real-world applications, neural network models are commonly deployed in dynamic environments, where the distribution of the target domain undergoes temporal changes. Continual Test-Time Adaptation (CTTA) has recently emerged as a promising techni
Externí odkaz:
http://arxiv.org/abs/2406.16439
Autor:
Zhang, Lixian, Zhao, Yi, Dong, Runmin, Zhang, Jinxiao, Yuan, Shuai, Cao, Shilei, Chen, Mengxuan, Zheng, Juepeng, Li, Weijia, Liu, Wei, Zhang, Wayne, Feng, Litong, Fu, Haohuan
Vast amounts of remote sensing (RS) data provide Earth observations across multiple dimensions, encompassing critical spatial, temporal, and spectral information which is essential for addressing global-scale challenges such as land use monitoring, d
Externí odkaz:
http://arxiv.org/abs/2406.08079
The objective of single image dehazing is to restore hazy images and produce clear, high-quality visuals. Traditional convolutional models struggle with long-range dependencies due to their limited receptive field size. While Transformers excel at ca
Externí odkaz:
http://arxiv.org/abs/2405.05811
3D building reconstruction from monocular remote sensing images is an important and challenging research problem that has received increasing attention in recent years, owing to its low cost of data acquisition and availability for large-scale applic
Externí odkaz:
http://arxiv.org/abs/2404.04823
This paper aims at achieving fine-grained building attribute segmentation in a cross-view scenario, i.e., using satellite and street-view image pairs. The main challenge lies in overcoming the significant perspective differences between street views
Externí odkaz:
http://arxiv.org/abs/2404.02638
Autor:
Pang, Chao, Wu, Jiang, Li, Jiayu, Liu, Yi, Sun, Jiaxing, Li, Weijia, Weng, Xingxing, Wang, Shuai, Feng, Litong, Xia, Gui-Song, He, Conghui
The generic large Vision-Language Models (VLMs) is rapidly developing, but still perform poorly in Remote Sensing (RS) domain, which is due to the unique and specialized nature of RS imagery and the comparatively limited spatial perception of current
Externí odkaz:
http://arxiv.org/abs/2403.20213
Autor:
Dong, Runmin, Yuan, Shuai, Luo, Bin, Chen, Mengxuan, Zhang, Jinxiao, Zhang, Lixian, Li, Weijia, Zheng, Juepeng, Fu, Haohuan
Reference-based super-resolution (RefSR) has the potential to build bridges across spatial and temporal resolutions of remote sensing images. However, existing RefSR methods are limited by the faithfulness of content reconstruction and the effectiven
Externí odkaz:
http://arxiv.org/abs/2403.17460
Despite CLIP being the foundation model in numerous vision-language applications, the CLIP suffers from a severe text spotting bias. Such bias causes CLIP models to `Parrot' the visual text embedded within images while disregarding the authentic visu
Externí odkaz:
http://arxiv.org/abs/2312.14232
Powered by the advances of optical remote sensing sensors, the production of very high spatial resolution multispectral images provides great potential for achieving cost-efficient and high-accuracy forest inventory and analysis in an automated way.
Externí odkaz:
http://arxiv.org/abs/2310.13481