Zobrazeno 1 - 10
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pro vyhledávání: '"Li Yansheng"'
Scene Graph Generation (SGG) aims to explore the relationships between objects in images and obtain scene summary graphs, thereby better serving downstream tasks. However, the long-tailed problem has adversely affected the scene graph's quality. The
Externí odkaz:
http://arxiv.org/abs/2407.19259
Autor:
Luo, Junwei, Pang, Zhen, Zhang, Yongjun, Wang, Tingzhu, Wang, Linlin, Dang, Bo, Lao, Jiangwei, Wang, Jian, Chen, Jingdong, Tan, Yihua, Li, Yansheng
Remote Sensing Large Multi-Modal Models (RSLMMs) are developing rapidly and showcase significant capabilities in remote sensing imagery (RSI) comprehension. However, due to the limitations of existing datasets, RSLMMs have shortcomings in understandi
Externí odkaz:
http://arxiv.org/abs/2406.10100
Autor:
Li, Yansheng, Wang, Linlin, Wang, Tingzhu, Yang, Xue, Luo, Junwei, Wang, Qi, Deng, Youming, Wang, Wenbin, Sun, Xian, Li, Haifeng, Dang, Bo, Zhang, Yongjun, Yu, Yi, Yan, Junchi
Scene graph generation (SGG) in satellite imagery (SAI) benefits promoting understanding of geospatial scenarios from perception to cognition. In SAI, objects exhibit great variations in scales and aspect ratios, and there exist rich relationships be
Externí odkaz:
http://arxiv.org/abs/2406.09410
Autor:
Tan, Jieyi, Li, Yansheng, Bartalev, Sergey A., Dang, Bo, Chen, Wei, Zhang, Yongjun, Yuan, Liangqi
Remote sensing semantic segmentation (RSS) is an essential task in Earth Observation missions. Due to data privacy concerns, high-quality remote sensing images with annotations cannot be well shared among institutions, making it difficult to fully ut
Externí odkaz:
http://arxiv.org/abs/2404.09292
Scene graph generation (SGG) aims to understand the visual objects and their semantic relationships from one given image. Until now, lots of SGG datasets with the eyelevel view are released but the SGG dataset with the overhead view is scarcely studi
Externí odkaz:
http://arxiv.org/abs/2404.07788
Autor:
Shao, Run, Yang, Cheng, Li, Qiujun, Zhu, Qing, Zhang, Yongjun, Li, YanSheng, Liu, Yu, Tang, Yong, Liu, Dapeng, Yang, Shizhong, Li, Haifeng
For a long time, due to the high heterogeneity in structure and semantics among various spatiotemporal modal data, the joint interpretation of multimodal spatiotemporal data has been an extremely challenging problem. The primary challenge resides in
Externí odkaz:
http://arxiv.org/abs/2401.00546
Autor:
Guo, Xin, Lao, Jiangwei, Dang, Bo, Zhang, Yingying, Yu, Lei, Ru, Lixiang, Zhong, Liheng, Huang, Ziyuan, Wu, Kang, Hu, Dingxiang, He, Huimei, Wang, Jian, Chen, Jingdong, Yang, Ming, Zhang, Yongjun, Li, Yansheng
Prior studies on Remote Sensing Foundation Model (RSFM) reveal immense potential towards a generic model for Earth Observation. Nevertheless, these works primarily focus on a single modality without temporal and geo-context modeling, hampering their
Externí odkaz:
http://arxiv.org/abs/2312.10115
Bridge detection in remote sensing images (RSIs) plays a crucial role in various applications, but it poses unique challenges compared to the detection of other objects. In RSIs, bridges exhibit considerable variations in terms of their spatial scale
Externí odkaz:
http://arxiv.org/abs/2312.02481
Global surface water detection in very-high-resolution (VHR) satellite imagery can directly serve major applications such as refined flood mapping and water resource assessment. Although achievements have been made in detecting surface water in small
Externí odkaz:
http://arxiv.org/abs/2303.09310
Semi-supervised semantic segmentation focuses on the exploration of a small amount of labeled data and a large amount of unlabeled data, which is more in line with the demands of real-world image understanding applications. However, it is still hinde
Externí odkaz:
http://arxiv.org/abs/2211.12425