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pro vyhledávání: '"Yang, Qize"'
Autor:
Wang, Huizheng, Fang, Jiahao, Tang, Xinru, Yue, Zhiheng, Li, Jinxi, Qin, Yubin, Guan, Sihan, Yang, Qize, Wang, Yang, Li, Chao, Hu, Yang, Yin, Shouyi
Benefiting from the self-attention mechanism, Transformer models have attained impressive contextual comprehension capabilities for lengthy texts. The requirements of high-throughput inference arise as the large language models (LLMs) become increasi
Externí odkaz:
http://arxiv.org/abs/2407.10416
Autor:
Fang, Jiahao, Wang, Huizheng, Yang, Qize, Kong, Dehao, Dai, Xu, Deng, Jinyi, Hu, Yang, Yin, Shouyi
Deep learning (DL) models are piquing high interest and scaling at an unprecedented rate. To this end, a handful of tiled accelerators have been proposed to support such large-scale training tasks. However, these accelerators often incorporate numero
Externí odkaz:
http://arxiv.org/abs/2406.03868
Autor:
Deng, Jinyi, Tang, Xinru, Yue, Zhiheng, Lu, Guangyang, Yang, Qize, Zhang, Jiahao, Li, Jinxi, Li, Chao, Wei, Shaojun, Hu, Yang, Yin, Shouyi
Given the increasing complexity of AI applications, traditional spatial architectures frequently fall short. Our analysis identifies a pattern of interconnected, multi-faceted tasks encompassing both AI and general computational processes. In respons
Externí odkaz:
http://arxiv.org/abs/2405.17221
Text-to-3D generation, which synthesizes 3D assets according to an overall text description, has significantly progressed. However, a challenge arises when the specific appearances need customizing at designated viewpoints but referring solely to the
Externí odkaz:
http://arxiv.org/abs/2404.06119
Autor:
Hu, Yang, Lin, Xinhan, Wang, Huizheng, He, Zhen, Yu, Xingmao, Zhang, Jiahao, Yang, Qize, Xu, Zheng, Guan, Sihan, Fang, Jiahao, Shang, Haoran, Tang, Xinru, Dai, Xu, Wei, Shaojun, Yin, Shouyi
Nowadays, artificial intelligence (AI) technology with large models plays an increasingly important role in both academia and industry. It also brings a rapidly increasing demand for the computing power of the hardware. As the computing demand for AI
Externí odkaz:
http://arxiv.org/abs/2310.09568
Autor:
Liu, Hanbing, He, Jun-Yan, Cheng, Zhi-Qi, Xiang, Wangmeng, Yang, Qize, Chai, Wenhao, Wang, Gaoang, Bao, Xu, Luo, Bin, Geng, Yifeng, Xie, Xuansong
Existing 3D human pose estimators face challenges in adapting to new datasets due to the lack of 2D-3D pose pairs in training sets. To overcome this issue, we propose \textit{Multi-Hypothesis \textbf{P}ose \textbf{Syn}thesis \textbf{D}omain \textbf{A
Externí odkaz:
http://arxiv.org/abs/2308.09678
Person re-identification (re-id), the process of matching pedestrian images across different camera views, is an important task in visual surveillance. Substantial development of re-id has recently been observed, and the majority of existing models a
Externí odkaz:
http://arxiv.org/abs/2002.02295
Autor:
Jiang, Xinyang, Gong, Yifei, Guo, Xiaowei, Yang, Qize, Huang, Feiyue, Zheng, Weishi, Zheng, Feng, Sun, Xing
Recently, the research interest of person re-identification (ReID) has gradually turned to video-based methods, which acquire a person representation by aggregating frame features of an entire video. However, existing video-based ReID methods do not
Externí odkaz:
http://arxiv.org/abs/1911.12512
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Akademický článek
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