Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Mei, Jilin"'
3D semantic occupancy prediction is an essential part of autonomous driving, focusing on capturing the geometric details of scenes. Off-road environments are rich in geometric information, therefore it is suitable for 3D semantic occupancy prediction
Externí odkaz:
http://arxiv.org/abs/2410.15792
Autor:
Min, Chen, Si, Shubin, Wang, Xu, Xue, Hanzhang, Jiang, Weizhong, Liu, Yang, Wang, Juan, Zhu, Qingtian, Zhu, Qi, Luo, Lun, Kong, Fanjie, Miao, Jinyu, Cai, Xudong, An, Shuai, Li, Wei, Mei, Jilin, Sun, Tong, Zhai, Heng, Liu, Qifeng, Zhao, Fangzhou, Chen, Liang, Wang, Shuai, Shang, Erke, Shang, Linzhi, Zhao, Kunlong, Li, Fuyang, Fu, Hao, Jin, Lei, Zhao, Jian, Mao, Fangyuan, Xiao, Zhipeng, Li, Chengyang, Dai, Bin, Zhao, Dawei, Xiao, Liang, Nie, Yiming, Hu, Yu, Li, Xuelong
Research on autonomous driving in unstructured outdoor environments is less advanced than in structured urban settings due to challenges like environmental diversities and scene complexity. These environments-such as rural areas and rugged terrains-p
Externí odkaz:
http://arxiv.org/abs/2410.07701
The limited training samples for object detectors commonly result in low accuracy out-of-distribution (OOD) object detection. We have observed that feature vectors of the same class tend to cluster tightly in feature space, whereas those of different
Externí odkaz:
http://arxiv.org/abs/2409.05466
In autonomous driving, 3D LiDAR plays a crucial role in understanding the vehicle's surroundings. However, the newly emerged, unannotated objects presents few-shot learning problem for semantic segmentation. This paper addresses the limitations of cu
Externí odkaz:
http://arxiv.org/abs/2408.15657
Infrared imaging technology has gained significant attention for its reliable sensing ability in low visibility conditions, prompting many studies to convert the abundant RGB images to infrared images. However, most existing image translation methods
Externí odkaz:
http://arxiv.org/abs/2407.09299
Based on the weight-sharing mechanism, one-shot NAS methods train a supernet and then inherit the pre-trained weights to evaluate sub-models, largely reducing the search cost. However, several works have pointed out that the shared weights suffer fro
Externí odkaz:
http://arxiv.org/abs/2302.14772
In autonomous driving, the novel objects and lack of annotations challenge the traditional 3D LiDAR semantic segmentation based on deep learning. Few-shot learning is a feasible way to solve these issues. However, currently few-shot semantic segmenta
Externí odkaz:
http://arxiv.org/abs/2302.08785
Publikováno v:
In Neurocomputing 28 June 2024 587
Autor:
Wang, Zhifang, Kou, Menglin, Deng, Qiyue, Yu, Haotian, Mei, Jilin, Gao, Jing, Fu, Wen, Ning, Baile
Publikováno v:
In Behavioural Brain Research 28 March 2024 462
Autor:
Ning, Baile, Wang, Zhifang, He, Jiangshan, Wu, Qian, Deng, Qiyue, Yang, Qing, Gao, Jing, Fu, Wen, Deng, Ying, Wu, Bingxin, Huang, Xichang, Mei, Jilin, Jiang, Fan, Fu, Wenbin
Publikováno v:
In Brain Research 1 January 2024 1822