Zobrazeno 1 - 10
of 365
pro vyhledávání: '"Liu, Shilei"'
Autor:
Zhao, Runkai, Heng, Yuwen, Wang, Heng, Gao, Yuanda, Liu, Shilei, Yao, Changhao, Chen, Jiawen, Cai, Weidong
Advanced Driver-Assistance Systems (ADAS) have successfully integrated learning-based techniques into vehicle perception and decision-making. However, their application in 3D lane detection for effective driving environment perception is hindered by
Externí odkaz:
http://arxiv.org/abs/2309.13596
Autor:
Zhu, Yin, Kong, Qiuqiang, Shi, Junjie, Liu, Shilei, Ye, Xuzhou, Wang, Ju-chiang, Zhang, Junping
Binaural rendering of ambisonic signals is of broad interest to virtual reality and immersive media. Conventional methods often require manually measured Head-Related Transfer Functions (HRTFs). To address this issue, we collect a paired ambisonic-bi
Externí odkaz:
http://arxiv.org/abs/2211.02301
Autor:
Kong, Qiuqiang, Liu, Shilei, Shi, Junjie, Ye, Xuzhou, Cao, Yin, Zhu, Qiaoxi, Xu, Yong, Wang, Yuxuan
Sound field decomposition predicts waveforms in arbitrary directions using signals from a limited number of microphones as inputs. Sound field decomposition is fundamental to downstream tasks, including source localization, source separation, and spa
Externí odkaz:
http://arxiv.org/abs/2210.12345
Autor:
Ren, Feiliang, Liu, Yongkang, Li, Bochao, Liu, Shilei, Wang, Bingchao, Wang, Jiaqi, Liu, Chunchao, Ma, Qi
Although existing machine reading comprehension models are making rapid progress on many datasets, they are far from robust. In this paper, we propose an understanding-oriented machine reading comprehension model to address three kinds of robustness
Externí odkaz:
http://arxiv.org/abs/2207.00187
Autor:
Ren, Feiliang, Liu, Yongkang, Li, Bochao, Wang, Zhibo, Guo, Yu, Liu, Shilei, Wu, Huimin, Wang, Jiaqi, Liu, Chunchao, Wang, Bingchao
Most existing multi-document machine reading comprehension models mainly focus on understanding the interactions between the input question and documents, but ignore following two kinds of understandings. First, to understand the semantic meaning of
Externí odkaz:
http://arxiv.org/abs/2204.03494
Autor:
Zhang, Ruiqian, Xing, Zhongfang, Geng, Shu, Yuan, Ling, Li, Xinhai, Lyu, Qiao, Yu, Huilan, Liu, Shilei
Publikováno v:
In Journal of Hazardous Materials 5 August 2024 474
Tagging based relational triple extraction methods are attracting growing research attention recently. However, most of these methods take a unidirectional extraction framework that first extracts all subjects and then extracts objects and relations
Externí odkaz:
http://arxiv.org/abs/2112.04940
Autor:
Ren, Feiliang, Zhang, Longhui, Yin, Shujuan, Zhao, Xiaofeng, Liu, Shilei, Li, Bochao, Liu, Yaduo
Table filling based relational triple extraction methods are attracting growing research interests due to their promising performance and their abilities on extracting triples from complex sentences. However, this kind of methods are far from their f
Externí odkaz:
http://arxiv.org/abs/2109.06705
Neural conversation models have shown great potentials towards generating fluent and informative responses by introducing external background knowledge. Nevertheless, it is laborious to construct such knowledge-grounded dialogues, and existing models
Externí odkaz:
http://arxiv.org/abs/2109.04096
Knowledge-grounded dialogue is a task of generating a fluent and informative response based on both conversation context and a collection of external knowledge, in which knowledge selection plays an important role and attracts more and more research
Externí odkaz:
http://arxiv.org/abs/2108.13686