1.
Harrer S, Shah P, Antony B, et al Artificial intelligence for clinical trial design.
Trends Pharmacol Sci.
2019;
40
(8):577–591. doi: 10.1016/j.tips.2019.05.005.
[
PubMed
] [
CrossRef
]
[
Google Scholar
]
2.
Wang H, Brown P C, Chow E C Y, et al 3D cell culture models: drug pharmacokinetics, safety assessment, and regulatory consideration.
Clin Transl Sci.
2021;
14
(5):1659–1680. doi: 10.1111/cts.13066.
[
PMC free article
]
[
PubMed
] [
CrossRef
]
[
Google Scholar
]
3.
Plagenhoef M R, Callahan P M, Beck W D, et al Aged rhesus monkeys: cognitive performance categorizations and preclinical drug testing.
Neuropharmacology.
2021;
187
:108489. doi: 10.1016/j.neuropharm.2021.108489.
[
PMC free article
]
[
PubMed
] [
CrossRef
]
[
Google Scholar
]
4.
Wu L, Wu D, Chen J, et al Intranasal salvinorin a improves neurological outcome in rhesus monkey ischemic stroke model using autologous blood clot.
J Cereb Blood Flow Metab.
2021;
41
(4):723–730. doi: 10.1177/0271678X20938137.
[
PMC free article
]
[
PubMed
] [
CrossRef
]
[
Google Scholar
]
5.
童安炀, 唐超, 王文剑 基于双流网络与支持向量机融合的人体行为识别
模式识别与人工智能
2021;
34
(9):863–870. doi: 10.16451/j.cnki.issn1003-6059.202109009.
[
CrossRef
]
[
Google Scholar
]
6.
Klaser A, Marszałek M, Schmid C. A spatio-temporal descriptor based on 3D-gradients//19th British Machine Vision Conference (BMVC), Leeds: British Machine Vision Association, 2008, 275: 1-10.
7.
Laptev I, Marszalek M, Schmid C, et al. Learning realistic human actions from movies//IEEE Conference on Computer Vision and Pattern Recognition, Alaska: IEEE, 2008. DOI: 10.1109/CVPR.2008.4587756.
8.
Dalal N, Triggs B, Schmid C. Human detection using oriented histograms of flow and appearance//European Conference on Computer Vision, Graz: IEEE, 2006: 428-441.
9.
Messing R, Pal C, Kautz H. Activity recognition using the velocity histories of tracked keypoints//2009 IEEE 12th International Conference on Computer Vision, Kyoto: IEEE, 2009: 104-111.
10.
Wang H, Klser A, Schmid C, et al Dense trajectories and motion boundary descriptors for action recognition.
International Journal of Computer Vision.
2013;
103
(1):60–79. doi: 10.1007/s11263-012-0594-8.
[
CrossRef
]
[
Google Scholar
]
11.
周波, 李俊峰 结合目标检测的人体行为识别
自动化学报
2020;
46
(9):1961–1970. doi: 10.16383/j.aas.c180848.
[
CrossRef
]
[
Google Scholar
]
12.
Simonyan K, Zisserman A. Two-stream convolutional networks for action recognition in videos//Advances in Neural Information Processing Systems, Montreal: IEEE, 2014: 568-576.
13.
Liu P, Lyu M, King I, et al. Selflow: self-supervised learning of optical flow//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, California: IEEE, 2019: 4571-4580.
14.
Tran D, Bourdev L, Fergus R. Learning spatiotemporal features with 3D convolutional networks//Proceedings of the IEEE International Conference on Computer Vision, Santiago: IEEE, 2015: 4489-4497.
15.
Christoph R, Pinz F A. Spatiotemporal residual networks for video action recognition//Advances in Neural Information Processing Systems, Barcelona: IEEE, 2016: 3468-3476.
16.
He K, Zhang X, Ren S. Deep residual learning for image recognition//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas: IEEE, 2016: 770-778
17.
Donahue J, Anne Hendricks L, Guadarrama S, et al. Long-term recurrent convolutional networks for visual recognition and description//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas: IEEE, 2015: 2625-2634.
18.
Feichtenhofer C, Fan H, Malik J. SlowFast networks for video recognition//Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul: IEEE, 2019: 6202-6211.
19.
Li D, Zhang K, Li Z, et al A spatiotemporal convolutional network for multi-behavior recognition of pigs.
Sensors (Basel)
2020;
20
(8):2381–2399. doi: 10.3390/s20082381.
[
PMC free article
]
[
PubMed
] [
CrossRef
]
[
Google Scholar
]
20.
Li C, Zhong Q, Xie D, et al. Skeleton-based action recognition with convolutional neural networks//2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), Hong Kong: IEEE, 2017: 597-600.
21.
Girdhar R, Carreira J, Doersch C, et al. Video action transformer network//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, California: IEEE, 2019: 244-253.
22.
Vaswani A, Shazeer N, Parmar N. Attention is all you need//Advances in Neural Information Processing Systems, California: IEEE, 2017: 5998-6008.
23.
Tao L, Wang X, Yamasaki T. Motion representation using residual frames with 3D CNN//IEEE International Conference on Image Processing, Abu Dhabi: IEEE, 2020: 1786-1790.
24.
Bala P C, Eisenreich B R, Yoo S B M, et al Automated markerless pose estimation in freely moving macaques with OpenMonkeyStudio.
Nat Commun.
2020;
11
(1):4560. doi: 10.1038/s41467-020-18441-5.
[
PMC free article
]
[
PubMed
] [
CrossRef
]
[
Google Scholar
]
25.
Tang D H, Wang C Y, Huang X, et al Inosine induces acute hyperuricaemia in rhesus monkey (Macaca mulatta) as a potential disease animal model.
Pharmaceutical Biology.
2021;
59
(1):175–182.
[
PMC free article
]
[
PubMed
]
[
Google Scholar
]
26.
Carreira J, Zisserman A. Quo vadis, action recognition? a new model and the kinetics dataset//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Venice: IEEE, 2017: 6299-6308.
27.
Tran D, Wang H, Torresani L, et al. A closer look at spatiotemporal convolutions for action recognition//Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, Salt Lake City and UT: IEEE, 2018: 6450-6459.
28.
Wang L, Xiong Y, Wang Z, et al. Temporal segment networks: towards good practices for deep action recognition//European Conference on Computer Vision, Amsterdam: IEEE, 2016: 20-36.
29.
Bertasius G, Wang H, Torresani L. Is space-time attention all you need for video understanding//International Conference on Machine Learning (ICMC), 2021. arXiv: 2102.05095.
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