Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Li, Daixun"'
Autor:
Li, Daixun, Xie, Weiying, Cao, Mingxiang, Wang, Yunke, Zhang, Jiaqing, Li, Yunsong, Fang, Leyuan, Xu, Chang
Multimodal image fusion and segmentation enhance scene understanding in autonomous driving by integrating data from various sensors. However, current models struggle to efficiently segment densely packed elements in such scenes, due to the absence of
Externí odkaz:
http://arxiv.org/abs/2408.13980
The rapid development of multimedia has provided a large amount of data with different distributions for visual tasks, forming different domains. Federated Learning (FL) can efficiently use this diverse data distributed on different client media in a
Externí odkaz:
http://arxiv.org/abs/2407.19174
Autor:
Zhang, Jiaqing, Cao, Mingxiang, Yang, Xue, Xie, Weiying, Lei, Jie, Li, Daixun, Huang, Wenbo, Li, Yunsong
Multimodal image fusion and object detection are crucial for autonomous driving. While current methods have advanced the fusion of texture details and semantic information, their complex training processes hinder broader applications. Addressing this
Externí odkaz:
http://arxiv.org/abs/2403.09323
In multimodal land cover classification (MLCC), a common challenge is the redundancy in data distribution, where irrelevant information from multiple modalities can hinder the effective integration of their unique features. To tackle this, we introdu
Externí odkaz:
http://arxiv.org/abs/2401.03179
Distributed deep learning has recently been attracting more attention in remote sensing (RS) applications due to the challenges posed by the increased amount of open data that are produced daily by Earth observation programs. However, the high commun
Externí odkaz:
http://arxiv.org/abs/2312.17530
Deep learning has driven significant progress in object detection using Synthetic Aperture Radar (SAR) imagery. Existing methods, while achieving promising results, often struggle to effectively integrate local and global information, particularly di
Externí odkaz:
http://arxiv.org/abs/2312.16943
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. While diffusion models have made great pro
Externí odkaz:
http://arxiv.org/abs/2401.02433
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. Deep neural networks can reveal the underlying distribution of mult
Externí odkaz:
http://arxiv.org/abs/2311.09540
High-dimensional images, known for their rich semantic information, are widely applied in remote sensing and other fields. The spatial information in these images reflects the object's texture features, while the spectral information reveals the pote
Externí odkaz:
http://arxiv.org/abs/2311.09520
Publikováno v:
IEEE Transactions on Circuits and Systems for Video Technology; October 2024, Vol. 34 Issue: 10 p10353-10367, 15p
