Zobrazeno 1 - 10
of 480
pro vyhledávání: '"CHUNHUA SHEN"'
Publikováno v:
Visual Informatics, Vol 7, Iss 1, Pp 30-40 (2023)
Self-supervised learning aims to learn a universal feature representation without labels. To date, most existing self-supervised learning methods are designed and optimized for image classification. These pre-trained models can be sub-optimal for den
Externí odkaz:
https://doaj.org/article/f14a80d8ec45483cb43184f7927cadde
Publikováno v:
Foods, Vol 13, Iss 1, p 27 (2023)
This work aimed to compare the aroma characteristics of representative brandies with different grades from Yantai (one of the Chinese core production areas) and Cognac and to establish relationships between sensory descriptors and chemical compositio
Externí odkaz:
https://doaj.org/article/44cade8d60794424ae14bfd46f2a442d
Publikováno v:
Plant Methods, Vol 15, Iss 1, Pp 1-14 (2019)
Abstract Background Grain yield of wheat is greatly associated with the population of wheat spikes, i.e., $$spike~number~\text {m}^{-2}$$ spikenumberm-2 . To obtain this index in a reliable and efficient way, it is necessary to count wheat spikes acc
Externí odkaz:
https://doaj.org/article/bf846ae2fbeb46b7be3e7febbece132a
Publikováno v:
Plant Methods, Vol 13, Iss 1, Pp 1-17 (2017)
Abstract Background Accurately counting maize tassels is important for monitoring the growth status of maize plants. This tedious task, however, is still mainly done by manual efforts. In the context of modern plant phenotyping, automating this task
Externí odkaz:
https://doaj.org/article/89ad99b0e1b14211b53f9f3497cea18c
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence. 45:7035-7049
In this work, we consider transferring the structure information from large networks to compact ones for dense prediction tasks in computer vision. Previous knowledge distillation strategies used for dense prediction tasks often directly borrow the d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2d7fd83ae6c8f16646101dd7a076bbf0
https://doi.org/10.1109/tpami.2020.3001940
https://doi.org/10.1109/tpami.2020.3001940
Autor:
Na Sai, James Paul Bockman, Hao Chen, Nathan Watson‐Haigh, Bo Xu, Xueying Feng, Adriane Piechatzek, Chunhua Shen, Matthew Gilliham
Publikováno v:
New Phytologist. 238:904-915
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::10d84b05ea1aeb015a2368a93cbe1ba1
https://doi.org/10.1111/nph.18765
https://doi.org/10.1111/nph.18765
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence. :1-14
Network pruning and quantization are proven to be effective ways for deep model compression. To obtain a highly compact model, most methods first perform network pruning and then conduct network quantization based on the pruned model. However, this s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e7d117317fc52b1b37f093e40ea2d718
https://doi.org/10.1109/tpami.2023.3275159
https://doi.org/10.1109/tpami.2023.3275159
Publikováno v:
In Pattern Recognition May 2017 65:238-250
Publikováno v:
Computer Graphics Forum. 41:347-358
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::34837505d5321c1f67bae046213aed64
https://doi.org/10.1111/cgf.14682
https://doi.org/10.1111/cgf.14682
Autor:
Yuan Zheng, Longman Li, Hong Cheng, Shengzhu Huang, Xiuming Feng, Lulu Huang, Luyun Wei, Dehao Cao, Sida Wang, Long Tian, Weijun Tang, Caitong He, Chunhua Shen, Bangzhu Luo, Maoling Zhu, Tao Liang, Baohong Pang, Mujun Li, Chaoqun Liu, Xing Chen, Fei Wang, Zengnan Mo, Xiaobo Yang
Publikováno v:
Environmental Science and Pollution Research. 29:85547-85558
Phthalates have been shown to have adverse effects on neurodevelopment, which may be gender-specific. However, the association between prenatal mixed exposure to phthalates and children's neurodevelopment remains inconsistent. We measured 15 prenatal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::62c7176d20712a6d55884cf55dcb2ff4
https://doi.org/10.1007/s11356-022-21769-8
https://doi.org/10.1007/s11356-022-21769-8