LiuGLChenZZChenR. Real-time S-T analysis method based on speaker recognition. In: 2020 2nd international conference on advanced control, automation and artificial intelligence(ACAAI 2020), Wuhan, Hubei, China, 12 January 2020.
2.
Wen B. The Application of Artificial Intelligence Technology in Physical Education. In: Yang CT., Pei Y., Chang JW. (eds) Innovative Computing. Lecture Notes in Electrical Engineering, Springer, Singapore.2020;675:pp 795-801.
3.
HuYGuXZhaoC.Online learning behavior analysis modeling and mining. Int J Open Educ Res2014;
20: 102–110.
4.
Ngoc AnhBTung SonNTruong LamP, et al.
A computer-vision based application for student behavior monitoring in classroom. Int J Appl Sci2019;
9: 4729.
5.
KimYSoyataTBehnaghRF.Towards emotionally aware AI smart classroom: current issues and directions for engineering and education. Int JIEEE Access2018;
6: 5308–5331.
6.
Ashwin TSGuddetiRMR.Automatic detection of students’ affective states in classroom environment using hybrid convolutional neural networks. Educ Inf Technol2020;
25: 1387–1415.
7.
PriyaDTUdayanJD.Affective emotion classification using feature vector of image based on visual concepts. Int J Electr Eng Educ. Epub ahead of print 2020. DOI: 10.1177/0020720920936834.
8.
YangDLiuXHeH, et al.
Air-to-ground multimodal object detection algorithm based on feature association learning. Int J Adv Robot Syst2019;
16: 1–9.
9.
WeiMHuangHHuY, et al.
Monocular vision target location and tracking method for multi rotor UAV based on deep learning. Comput Meas Contr2020;
28: 156–160.
10.
YimJJooDBaeJ, et al. A gift from knowledge distillation: fast optimization, network minimization and transfer learning. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), Honolulu, HI, USA, IEEE Computer Society 2017, pp.7130–7138.
11.
ParkWKimDLuY, et al. Relational knowledge distillation. In: 2019 IEEE/CVF conference on computer vision and pattern recognition (CVPR), Long Beach, CA, USA, USA, IEEE Computer Society 2019, pp.3967–3976.
12.
IandolaFNHanSMoskewiczMW, et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size. Computer Science 2017. Available at: ArXiv abs/1602.07360.
13.
KimWJungWSChoiHK.Lightweight driver monitoring system based on multi-task mobilenets. Sensors2019;
19: 3200.
14.
ZhangXZhouXLinM, et al. ShuffleNet: an extremely efficient convolutional neural network for mobile devices. In: 2018 IEEE/CVF conference on computer vision and pattern recognition (CVPR), Salt Lake City, USA,IEEE Computer Society 2018, pp.6848–6856.
15.
CholletF. Xception: deep learning with depthwise separable convolutions. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), Honolulu, HI, USA, IEEE Computer Society 2017, pp.1800–1807.
16.
HowardAGZhuMChenB, et al. MobileNets: efficient convolutional neural networks for mobile vision applications. Published 2017 Computer Science ArXiv; ArXiv abs/1704.04861 (2017): n. pag.
17.
YanZLiXLiM, et al. Shift-Net: image inpainting via deep feature rearrangement.In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018, Munich, Germany. Springer, Cham. Computer Vision – ECCV 2018, vol 11218, pp. 3–19.
18.
MaNZhangXZhengHT, et al. ShuffleNetV2: practical guidelines for efficient CNN architecture design. In: Proceedings of the European conference on computer vision (ECCV), vol. 31, 2018, pp.10392–10402.
19.
SandlerMHowardAZhuM, et al. MobileNetV2: inverted residuals and linear bottlenecks. In: 2018 IEEE/CVF conference on computer vision and pattern recognition (CVPR), Salt Lake City, UT, USA, 18–23 June 2018, pp.4510–4520. New York: IEEE.
20.
IoannouYRobertsonDCipollaR, et al. Deep roots: improving CNN efficiency with hierarchical filter groups. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), Honolulu, HI, USA, 2017, pp.5977–5986.
21.
SzegedyCIoffeSVanhouckeV, et al. Inception-v4, Inception-Resnetand the impact of residual connections on learning. In: AAAI’17: Proceedings of the thirty-first AAAI conference on artificial intelligence, February 2017, San Francisco, California, USA, AAAI Conference on Artificial Intelligence, pp.4278–4284.
22.
ZophBVasudevanVShlensJ, et al. Learning transferable architectures for scalable image recognition. In: 2018 IEEE/CVF Conference on computer vision and pattern recognition (CVPR), Salt Lake City, USA, IEEE Computer Society 2018, pp.8697–8710 .
23.
HuangGLiuZMaatenLVD, et al. Densely connected convolutional networks. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), Honolulu, HI, USA, 21–26 July 2017, IEEE Computer Society, pp.2261–2269.
24.
HuangGLiuSLaurensVD, et al. CondenseNet: an efficient DenseNet using learned group convolutions. In: 2018 IEEE/CVF conference on computer vision and pattern recognition (CVPR), Salt Lake City, UT, USA, 18–23 June 2018, IEEE Computer Society, pp.2752–2761.
25.
EveringhamMvan GoolLWilliamsCK, et al.
The pascal visual object classes (VOC) challenge. Int J Comput Vis2010;
88: 303–338.
26.
RenSHeKGirshickR, et al.
Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell2017;
39: 1137–1149.
27.
BangqiLXinyiLTingtingY, et al.
Research on innovation and application of subject teaching mode based on smart classroom. Int J Audio-Visual Educ Res2019; 40: 85–91.
28.
TonguçGOzaydın OzkaraB.Automatic recognition of student emotions from facial expressions during a lecture. Int J Comput Educ2020;
148: 0360–1315.