Zobrazeno 1 - 10
of 431
pro vyhledávání: '"Heterogeneous feature fusion"'
Task-specific data-fusion networks have marked considerable achievements in urban scene parsing. Among these networks, our recently proposed RoadFormer successfully extracts heterogeneous features from RGB images and surface normal maps and fuses the
Externí odkaz:
http://arxiv.org/abs/2407.21631
Data-fusion networks have shown significant promise for RGB-thermal scene parsing. However, the majority of existing studies have relied on symmetric duplex encoders for heterogeneous feature extraction and fusion, paying inadequate attention to the
Externí odkaz:
http://arxiv.org/abs/2404.03527
Feature-fusion networks with duplex encoders have proven to be an effective technique to solve the freespace detection problem. However, despite the compelling results achieved by previous research efforts, the exploration of adequate and discriminat
Externí odkaz:
http://arxiv.org/abs/2402.18918
Autor:
Guo, Zhiyang1 (AUTHOR) 710980746@qq.Com, Hu, Xing2 (AUTHOR), Wang, Jiejia1 (AUTHOR), Miao, XiaoYu1 (AUTHOR), Sun, MengTeng1 (AUTHOR), Wang, HuaiWei1 (AUTHOR), Ma, XueYing1 (AUTHOR)
Publikováno v:
Scientific Reports. 7/29/2024, Vol. 14 Issue 1, p1-13. 13p.
Akademický článek
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Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract Detecting roads in automatic driving environments poses a challenge due to issues such as boundary fuzziness, occlusion, and glare from light. We believe that two factors are instrumental in addressing these challenges and enhancing detectio
Externí odkaz:
https://doaj.org/article/e0cb5c20f20c43cfbf57dc365dcde6ea
Akademický článek
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Publikováno v:
In Information Fusion March 2024 103
Publikováno v:
IEEE Access, Vol 12, Pp 4166-4177 (2024)
Traffic flow prediction is a crucial aspect of Intelligent Transport Systems, offering a scientific foundation for urban transport system management and planning. However, predicting traffic flow becomes challenging due to its susceptibility to diver
Externí odkaz:
https://doaj.org/article/92638cd2846e44a9919989ae09b0f79f
Publikováno v:
Applied Sciences, Vol 14, Iss 20, p 9256 (2024)
The joint extraction of entities and relations is a critical task in information extraction, and its performance directly affects the performance of downstream tasks. However, existing joint extraction models based on deep learning exhibit weak proce
Externí odkaz:
https://doaj.org/article/42bdb10cb9ed4cf5a1d76dcbc0605e61