Zobrazeno 1 - 10
of 13 610
pro vyhledávání: '"WU, Jin"'
The stability of visual odometry (VO) systems is undermined by degraded image quality, especially in environments with significant illumination changes. This study employs a deep reinforcement learning (DRL) framework to train agents for exposure con
Externí odkaz:
http://arxiv.org/abs/2408.17005
In autonomous driving, accurately distinguishing between static and moving objects is crucial for the autonomous driving system. When performing the motion object segmentation (MOS) task, effectively leveraging motion information from objects becomes
Externí odkaz:
http://arxiv.org/abs/2408.13790
Autor:
Wang, Jingquan, Zhang, Harry, Unjhawala, Huzaifa Mustafa, Negrut, Peter, Wang, Shu, Slaton, Khailanii, Serban, Radu, Wu, Jin-Long, Negrut, Dan
We introduce SimBench, a benchmark designed to evaluate the proficiency of student large language models (S-LLMs) in generating digital twins (DTs) that can be used in simulators for virtual testing. Given a collection of S-LLMs, this benchmark enabl
Externí odkaz:
http://arxiv.org/abs/2408.11987
Autor:
Li, Yang, Cai, Wen-Qi, Ren, Ji-Gang, Wang, Chao-Ze, Yang, Meng, Zhang, Liang, Wu, Hui-Ying, Chang, Liang, Wu, Jin-Cai, Jin, Biao, Xue, Hua-Jian, Li, Xue-Jiao, Liu, Hui, Yu, Guang-Wen, Tao, Xue-Ying, Chen, Ting, Liu, Chong-Fei, Luo, Wen-Bin, Zhou, Jie, Yong, Hai-Lin, Li, Yu-Huai, Li, Feng-Zhi, Jiang, Cong, Chen, Hao-Ze, Wu, Chao, Tong, Xin-Hai, Xie, Si-Jiang, Zhou, Fei, Liu, Wei-Yue, Liu, Nai-Le, Li, Li, Xu, Feihu, Cao, Yuan, Yin, Juan, Shu, Rong, Wang, Xiang-Bin, Zhang, Qiang, Wang, Jian-Yu, Liao, Sheng-Kai, Peng, Cheng-Zhi, Pan, Jian-Wei
A quantum network provides an infrastructure connecting quantum devices with revolutionary computing, sensing, and communication capabilities. As the best-known application of a quantum network, quantum key distribution (QKD) shares secure keys guara
Externí odkaz:
http://arxiv.org/abs/2408.10994
Large-scale multi-session LiDAR mapping is essential for a wide range of applications, including surveying, autonomous driving, crowdsourced mapping, and multi-agent navigation. However, existing approaches often struggle with data redundancy, robust
Externí odkaz:
http://arxiv.org/abs/2408.03723
Closure models are widely used in simulating complex multiscale dynamical systems such as turbulence and the earth system, for which direct numerical simulation that resolves all scales is often too expensive. For those systems without a clear scale
Externí odkaz:
http://arxiv.org/abs/2408.02965
$P_c(4457)$ has been discovered over five years, but the parity of this particle remains undetermined. In this letter we propose a new interpretation for $P_c(4457)$, which is the state generated from the coupled-channel $\bar{D}^0\Lambda_c^{+}(2595)
Externí odkaz:
http://arxiv.org/abs/2407.05743
Publikováno v:
In: Proceedings of the 27th European Conference on Artificial Intelligence, 2024
Random forests are classical ensemble algorithms that construct multiple randomized decision trees and aggregate their predictions using naive averaging. \citet{zhou2019deep} further propose a deep forest algorithm with multi-layer forests, which out
Externí odkaz:
http://arxiv.org/abs/2407.05108
Despite advancements in robotic-assisted surgery, automating complex tasks like suturing remain challenging due to the need for adaptability and precision. Learning-based approaches, particularly reinforcement learning (RL) and imitation learning (IL
Externí odkaz:
http://arxiv.org/abs/2406.13865
Large-scale multi-session LiDAR mapping is crucial for various applications but still faces significant challenges in data redundancy, memory consumption, and efficiency. This paper presents MS-Mapping, a novel multi-session LiDAR mapping system that
Externí odkaz:
http://arxiv.org/abs/2406.02096