Zobrazeno 1 - 10
of 7 189
pro vyhledávání: '"Yin, Wei"'
Autor:
Jiang, Bo, Chen, Shaoyu, Liao, Bencheng, Zhang, Xingyu, Yin, Wei, Zhang, Qian, Huang, Chang, Liu, Wenyu, Wang, Xinggang
End-to-end autonomous driving demonstrates strong planning capabilities with large-scale data but still struggles in complex, rare scenarios due to limited commonsense. In contrast, Large Vision-Language Models (LVLMs) excel in scene understanding an
Externí odkaz:
http://arxiv.org/abs/2410.22313
Autor:
Dwivedi, Vijay Prakash, Schlegel, Viktor, Liu, Andy T., Nguyen, Thanh-Tung, Kashyap, Abhinav Ramesh, Wei, Jeng, Yin, Wei-Hsian, Winkler, Stefan, Tan, Robby T.
Large Language Models (LLMs) have demonstrated remarkable performance across various domains, including healthcare. However, their ability to effectively represent structured non-textual data, such as the alphanumeric medical codes used in records li
Externí odkaz:
http://arxiv.org/abs/2410.13351
Autor:
Gu, Songen, Yin, Wei, Jin, Bu, Guo, Xiaoyang, Wang, Junming, Li, Haodong, Zhang, Qian, Long, Xiaoxiao
We propose DOME, a diffusion-based world model that predicts future occupancy frames based on past occupancy observations. The ability of this world model to capture the evolution of the environment is crucial for planning in autonomous driving. Comp
Externí odkaz:
http://arxiv.org/abs/2410.10429
Autor:
Yang, Honghui, Huang, Di, Yin, Wei, Shen, Chunhua, Liu, Haifeng, He, Xiaofei, Lin, Binbin, Ouyang, Wanli, He, Tong
Video depth estimation has long been hindered by the scarcity of consistent and scalable ground truth data, leading to inconsistent and unreliable results. In this paper, we introduce Depth Any Video, a model that tackles the challenge through two ke
Externí odkaz:
http://arxiv.org/abs/2410.10815
Autor:
Wang, Junming, Zhang, Xingyu, Xing, Zebin, Gu, Songen, Guo, Xiaoyang, Hu, Yang, Song, Ziying, Zhang, Qian, Long, Xiaoxiao, Yin, Wei
In this paper, we propose HE-Drive: the first human-like-centric end-to-end autonomous driving system to generate trajectories that are both temporally consistent and comfortable. Recent studies have shown that imitation learning-based planners and l
Externí odkaz:
http://arxiv.org/abs/2410.05051
Autor:
Wang, Junming, Yin, Wei, Long, Xiaoxiao, Zhang, Xingyu, Xing, Zebin, Guo, Xiaoyang, Zhang, Qian
3D semantic occupancy prediction networks have demonstrated remarkable capabilities in reconstructing the geometric and semantic structure of 3D scenes, providing crucial information for robot navigation and autonomous driving systems. However, due t
Externí odkaz:
http://arxiv.org/abs/2409.19987
Autor:
He, Jing, Li, Haodong, Yin, Wei, Liang, Yixun, Li, Leheng, Zhou, Kaiqiang, Zhang, Hongbo, Liu, Bingbing, Chen, Ying-Cong
Leveraging the visual priors of pre-trained text-to-image diffusion models offers a promising solution to enhance zero-shot generalization in dense prediction tasks. However, existing methods often uncritically use the original diffusion formulation,
Externí odkaz:
http://arxiv.org/abs/2409.18124
Autor:
Popescu, Cristian D., Yin, Wei
Greither and Kurihara proved a theorem about the commutativity of projective limits and Fitting ideals for modules over the classical equivariant Iwasawa algebra $\Lambda_G=\mathbb{Z}_p[[T]][G]$, where $G$ is a finite, abelian group and $\Bbb Z_p$ is
Externí odkaz:
http://arxiv.org/abs/2409.11562
Autor:
Wang, Linhan, Cheng, Kai, Lei, Shuo, Wang, Shengkun, Yin, Wei, Lei, Chenyang, Long, Xiaoxiao, Lu, Chang-Tien
We present DC-Gaussian, a new method for generating novel views from in-vehicle dash cam videos. While neural rendering techniques have made significant strides in driving scenarios, existing methods are primarily designed for videos collected by aut
Externí odkaz:
http://arxiv.org/abs/2405.17705
Publikováno v:
SIGMA 20 (2024), 091, 14 pages
In the present paper, an integrable semi-discretization of the modified Camassa-Holm (mCH) equation with cubic nonlinearity is presented. The key points of the construction are based on the discrete Kadomtsev-Petviashvili (KP) equation and appropriat
Externí odkaz:
http://arxiv.org/abs/2404.18372