Zobrazeno 1 - 10
of 263
pro vyhledávání: '"Han, Zeyu"'
Autor:
Jiang, Junkai, Han, Zeyu, Wang, Yuning, Cai, Mengchi, Meng, Qingwen, Xu, Qing, Wang, Jianqiang
Driving risk assessment is crucial for both autonomous vehicles and human-driven vehicles. The driving risk can be quantified as the product of the probability that an event (such as collision) will occur and the consequence of that event. However, t
Externí odkaz:
http://arxiv.org/abs/2410.14996
Autor:
Zhu, Zehang, Wang, Yuning, Ke, Tianqi, Han, Zeyu, Xu, Shaobing, Xu, Qing, Dolan, John M., Wang, Jianqiang
Safety is one of the most crucial challenges of autonomous driving vehicles, and one solution to guarantee safety is to employ an additional control revision module after the planning backbone. Control Barrier Function (CBF) has been widely used beca
Externí odkaz:
http://arxiv.org/abs/2409.14688
Autor:
Han, Zeyu, Jiang, Junkai, Ding, Xiaokang, Meng, Qingwen, Xu, Shaobing, He, Lei, Wang, Jianqiang
The 4D millimeter-wave (mmWave) radar, with its robustness in extreme environments, extensive detection range, and capabilities for measuring velocity and elevation, has demonstrated significant potential for enhancing the perception abilities of aut
Externí odkaz:
http://arxiv.org/abs/2405.05131
Autor:
He, Lei, Li, Leheng, Sun, Wenchao, Han, Zeyu, Liu, Yichen, Zheng, Sifa, Wang, Jianqiang, Li, Keqiang
Neural Radiance Field (NeRF) has garnered significant attention from both academia and industry due to its intrinsic advantages, particularly its implicit representation and novel view synthesis capabilities. With the rapid advancements in deep learn
Externí odkaz:
http://arxiv.org/abs/2404.13816
Autor:
Ren, Bin, Li, Yawei, Mehta, Nancy, Timofte, Radu, Yu, Hongyuan, Wan, Cheng, Hong, Yuxin, Han, Bingnan, Wu, Zhuoyuan, Zou, Yajun, Liu, Yuqing, Li, Jizhe, He, Keji, Fan, Chao, Zhang, Heng, Zhang, Xiaolin, Yin, Xuanwu, Zuo, Kunlong, Liao, Bohao, Xia, Peizhe, Peng, Long, Du, Zhibo, Di, Xin, Li, Wangkai, Wang, Yang, Zhai, Wei, Pei, Renjing, Guo, Jiaming, Xu, Songcen, Cao, Yang, Zha, Zhengjun, Wang, Yan, Liu, Yi, Wang, Qing, Zhang, Gang, Zhang, Liou, Zhao, Shijie, Sun, Long, Pan, Jinshan, Dong, Jiangxin, Tang, Jinhui, Liu, Xin, Yan, Min, Wang, Qian, Zhou, Menghan, Yan, Yiqiang, Liu, Yixuan, Chan, Wensong, Tang, Dehua, Zhou, Dong, Wang, Li, Tian, Lu, Emad, Barsoum, Jia, Bohan, Qiao, Junbo, Zhou, Yunshuai, Zhang, Yun, Li, Wei, Lin, Shaohui, Zhou, Shenglong, Chen, Binbin, Liao, Jincheng, Zhao, Suiyi, Zhang, Zhao, Wang, Bo, Luo, Yan, Wei, Yanyan, Li, Feng, Wang, Mingshen, Guan, Jinhan, Hu, Dehua, Yu, Jiawei, Xu, Qisheng, Sun, Tao, Lan, Long, Xu, Kele, Lin, Xin, Yue, Jingtong, Yang, Lehan, Du, Shiyi, Qi, Lu, Ren, Chao, Han, Zeyu, Wang, Yuhan, Chen, Chaolin, Li, Haobo, Zheng, Mingjun, Yang, Zhongbao, Song, Lianhong, Yan, Xingzhuo, Fu, Minghan, Zhang, Jingyi, Li, Baiang, Zhu, Qi, Xu, Xiaogang, Guo, Dan, Guo, Chunle, Chen, Jiadi, Long, Huanhuan, Duanmu, Chunjiang, Lei, Xiaoyan, Liu, Jie, Jia, Weilin, Cao, Weifeng, Zhang, Wenlong, Mao, Yanyu, Guo, Ruilong, Zhang, Nihao, Pandey, Manoj, Chernozhukov, Maksym, Le, Giang, Cheng, Shuli, Wang, Hongyuan, Wei, Ziyan, Tang, Qingting, Wang, Liejun, Li, Yongming, Guo, Yanhui, Xu, Hao, Khatami-Rizi, Akram, Mahmoudi-Aznaveh, Ahmad, Hsu, Chih-Chung, Lee, Chia-Ming, Chou, Yi-Shiuan, Joshi, Amogh, Akalwadi, Nikhil, Malagi, Sampada, Yashaswini, Palani, Desai, Chaitra, Tabib, Ramesh Ashok, Patil, Ujwala, Mudenagudi, Uma
This paper provides a comprehensive review of the NTIRE 2024 challenge, focusing on efficient single-image super-resolution (ESR) solutions and their outcomes. The task of this challenge is to super-resolve an input image with a magnification factor
Externí odkaz:
http://arxiv.org/abs/2404.10343
Large models represent a groundbreaking advancement in multiple application fields, enabling remarkable achievements across various tasks. However, their unprecedented scale comes with significant computational costs. These models, often consisting o
Externí odkaz:
http://arxiv.org/abs/2403.14608
Zero-shot referring expression comprehension aims at localizing bounding boxes in an image corresponding to provided textual prompts, which requires: (i) a fine-grained disentanglement of complex visual scene and textual context, and (ii) a capacity
Externí odkaz:
http://arxiv.org/abs/2311.17048
Autor:
Han, Zeyu, Wang, Yuhan, Zhou, Luping, Wang, Peng, Yan, Binyu, Zhou, Jiliu, Wang, Yan, Shen, Dinggang
To obtain high-quality positron emission tomography (PET) scans while reducing radiation exposure to the human body, various approaches have been proposed to reconstruct standard-dose PET (SPET) images from low-dose PET (LPET) images. One widely adop
Externí odkaz:
http://arxiv.org/abs/2308.10157
Recently, deep learning (DL) has automated and accelerated the clinical radiation therapy (RT) planning significantly by predicting accurate dose maps. However, most DL-based dose map prediction methods are data-driven and not applicable for cervical
Externí odkaz:
http://arxiv.org/abs/2308.10142
Autor:
Xu, Binfeng, Liu, Xukun, Shen, Hua, Han, Zeyu, Li, Yuhan, Yue, Murong, Peng, Zhiyuan, Liu, Yuchen, Yao, Ziyu, Xu, Dongkuan
Augmented Language Models (ALMs) empower large language models with the ability to use tools, transforming them into intelligent agents for real-world interactions. However, most existing frameworks for ALMs, to varying degrees, are deficient in the
Externí odkaz:
http://arxiv.org/abs/2308.04030