Zobrazeno 1 - 10
of 688
pro vyhledávání: '"Wang, PengYuan"'
Autor:
Zhang, Zhilong, Chen, Ruifeng, Ye, Junyin, Sun, Yihao, Wang, Pengyuan, Pang, Jingcheng, Li, Kaiyuan, Liu, Tianshuo, Lin, Haoxin, Yu, Yang, Zhou, Zhi-Hua
World models play a crucial role in decision-making within embodied environments, enabling cost-free explorations that would otherwise be expensive in the real world. To facilitate effective decision-making, world models must be equipped with strong
Externí odkaz:
http://arxiv.org/abs/2411.05619
Autor:
Jin, Xin, Guo, Chunle, Li, Xiaoming, Yue, Zongsheng, Li, Chongyi, Zhou, Shangchen, Feng, Ruicheng, Dai, Yuekun, Yang, Peiqing, Loy, Chen Change, Li, Ruoqi, Liu, Chang, Wang, Ziyi, Du, Yao, Yang, Jingjing, Bao, Long, Sun, Heng, Kong, Xiangyu, Xing, Xiaoxia, Wu, Jinlong, Xue, Yuanyang, Park, Hyunhee, Song, Sejun, Kim, Changho, Tan, Jingfan, Luo, Wenhan, Liu, Zikun, Qiao, Mingde, Jiang, Junjun, Jiang, Kui, Xiao, Yao, Sun, Chuyang, Hu, Jinhui, Ruan, Weijian, Dong, Yubo, Chen, Kai, Jo, Hyejeong, Qin, Jiahao, Han, Bingjie, Qin, Pinle, Chai, Rui, Wang, Pengyuan
The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems. However, the scarcity of high-quality data fo
Externí odkaz:
http://arxiv.org/abs/2406.07006
Large language models (LLMs) have catalyzed a paradigm shift in natural language processing, yet their limited controllability poses a significant challenge for downstream applications. We aim to address this by drawing inspiration from the neural me
Externí odkaz:
http://arxiv.org/abs/2405.17039
Autor:
Pang, Jing-Cheng, Fan, Heng-Bo, Wang, Pengyuan, Xiao, Jia-Hao, Tang, Nan, Yang, Si-Hang, Jia, Chengxing, Huang, Sheng-Jun, Yu, Yang
The rise of large language models (LLMs) has revolutionized the way that we interact with artificial intelligence systems through natural language. However, LLMs often misinterpret user queries because of their uncertain intention, leading to less he
Externí odkaz:
http://arxiv.org/abs/2402.03719
Category-level pose estimation is a challenging task with many potential applications in computer vision and robotics. Recently, deep-learning-based approaches have made great progress, but are typically hindered by the need for large datasets of eit
Externí odkaz:
http://arxiv.org/abs/2311.13777
Autor:
Di, Yan, Zhang, Chenyangguang, Wang, Pengyuan, Zhai, Guangyao, Zhang, Ruida, Manhardt, Fabian, Busam, Benjamin, Ji, Xiangyang, Tombari, Federico
In this paper, we present a novel shape reconstruction method leveraging diffusion model to generate 3D sparse point cloud for the object captured in a single RGB image. Recent methods typically leverage global embedding or local projection-based fea
Externí odkaz:
http://arxiv.org/abs/2308.07837
Autor:
Pang, Jing-Cheng, Wang, Pengyuan, Li, Kaiyuan, Chen, Xiong-Hui, Xu, Jiacheng, Zhang, Zongzhang, Yu, Yang
Large Language Models (LLMs) have exhibited remarkable performance across various natural language processing (NLP) tasks. However, fine-tuning these models often necessitates substantial supervision, which can be expensive and time-consuming to obta
Externí odkaz:
http://arxiv.org/abs/2305.14483
Publikováno v:
Journal of Marketing Research (JMR). Feb2024, Vol. 61 Issue 1, p70-91. 22p.
Publikováno v:
Journal of Marketing; Jan2025, Vol. 89 Issue 1, p77-93, 17p
Autor:
Wang, Pengyuan1 (AUTHOR) wpengyuan@zzuli.edu.cn, Wen, Zhengying2 (AUTHOR)
Publikováno v:
Scientific Reports. 12/28/2024, Vol. 14 Issue 1, p1-18. 18p.