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pro vyhledávání: '"Wen, Sihan"'
In recent years, a plethora of diverse methods have been proposed for 3D pose estimation. Among these, self-attention mechanisms and graph convolutions have both been proven to be effective and practical methods. Recognizing the strengths of those tw
Externí odkaz:
http://arxiv.org/abs/2406.01196
Deep learning based image compression methods have achieved superior performance compared with transform based conventional codec. With end-to-end Rate-Distortion Optimization (RDO) in the codec, compression model is optimized with Lagrange multiplie
Externí odkaz:
http://arxiv.org/abs/2004.05855
We propose an end-to-end trainable image compression framework with a multi-scale and context-adaptive entropy model, especially for low bitrate compression. Due to the success of autoregressive priors in probabilistic generative model, the complemen
Externí odkaz:
http://arxiv.org/abs/1910.07844
Publikováno v:
In Progress in Natural Science: Materials International February 2022 32(1):43-53
Influence of different rolling processes on microstructure and strength of the Al–Cu–Li alloy AA2195
Autor:
Ye, Fan, Mao, Ling, Rong, Jian, Zhang, Baoshuai, Wei, Lijun, Wen, Sihan, Jiao, Haojun, Wu, Sujun
Publikováno v:
In Progress in Natural Science: Materials International February 2022 32(1):87-95
Publikováno v:
In Journal of Manufacturing Processes August 2021 68 Part A:1261-1270
Akademický článek
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Autor:
Pi, Menghan, Qin, Shanhe, Wen, Sihan, Wang, Zhisen, Wang, Xiaoyu, Li, Min, Lu, Honglang, Meng, Qingdang, Cui, Wei, Ran, Rong
Publikováno v:
Advanced Functional Materials; 1/3/2023, Vol. 33 Issue 1, p1-15, 15p
Publikováno v:
CVPR Workshops
Deep learning based image compression methods have achieved superior performance compared with transform based conventional codec. With end-to-end Rate-Distortion Optimization (RDO) in the codec, compression model is optimized with Lagrange multiplie
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ea3ecfe51994f736676eef73a1664a60
http://arxiv.org/abs/2004.05855
http://arxiv.org/abs/2004.05855
Publikováno v:
Journal of Manufacturing Processes; 20210101, Issue: Preprints