Object Classification Based on Multitask Convolutional Neural Network

Miaohui Zhang; Bo Zhang; Chengcheng Gao

doi:10.3788/LOP56.231502

[1] Al-Saffar A A M, Tao H, Talab M A. Review of deep convolution neural network in image classification. [C]∥2017 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET), October 23-24, 2017, Jakarta, Indonesia. New York: IEEE, 26-31(2017).

[2] He Z C, Zhao L Z, Chen C. Convolution neural network with multi-resolution feature fusion for facial expression recognition[J]. Laser & Optoelectronics Progress, 55, 071503(2018).

[3] Li J N, Zhang B H. Face recognition by feature matching fusion combined with improved convolutional neural network[J]. Laser & Optoelectronics Progress, 55, 101504(2018).

[4] Hasan M S. An application of pre-trained CNN for image classification. [C]∥2017 20th International Conference of Computer and Information Technology (ICCIT), December 22-24, 2017, Dhaka, Bangladesh. New York: IEEE, 17575567(2017).

[5] Yang P, Zhao P L, Gao X. Robust online multi-task learning with correlative and personalized structures[J]. IEEE Transactions on Knowledge and Data Engineering, 29, 2510-2521(2017).

[6] Sun Y, Chen Y H, Wang X G et al. Deep learning face representation by joint identification-verification. [C]∥Proceedings of the 27th International Conference on Neural Information Processing Systems, December 8-13, 2014, Montreal, Canada. Canada: NIPS, 1988-1996(2014).

[7] Zhang Z P, Luo P, Loy C C et al. Facial landmark detection by deep multi-task learning[M]. ∥Fleet D, Pajdla T, Schiele B, et al. Computer vision-ECCV 2014. Lecture notes in computer science. Cham: Springer, 8694, 94-108(2014).

[8] Trottier L, Giguère P. -05-15)[2019-04-11]. https:∥arxiv., org/abs/1711, 00111(2018).

[9] Ren S Q, He K M, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).

[10] Zhang N, Donahue J, Girshick R et al. Part-based R-CNNs for fine-grained category detection[M]. ∥Fleet D, Pajdla T, Schiele B, et al. Computer vision-ECCV 2014. Lecture notes in computer science. Cham: Springer, 8689, 834-849(2014).

[11] Peng Y X, He X T, Zhao J J. Object-part attention model for fine-grained image classification[J]. IEEE Transactions on Image Processing, 27, 1487-1500(2018).

[12] He K, Gkioxari G, Dollár P et al. Mask R-CNN. [C]∥The IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 2961-2969(2017).

[13] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. [C]∥Proceedings of the 25th International Conference on Neural Information Processing Systems, December 3-6, 2012, Lake Tahoe, Nevada Canada: NIPS, 1097-1105(2012).

[14] Szegedy C, Liu W, Jia Y Q et al. Going deeper with convolutions. [C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 15523970(2015).

[15] Simonyan K. -04-10)[2019-04-11]. https:∥arxiv., org/abs/1409, 1556(2015).

[16] Zhang C, Bengio S, Hardt M et al. -02-26)[2019-04-11]. https:∥arxiv., org/abs/1611, 03530(2017).

[17] Zhang J, Zhao H D, Li Y H et al. Classifier for recognition of fine-grained vehicle models under complex background[J]. Laser & Optoelectronics Progress, 56, 041501(2019).

[18] Zhang S L, Xie L B. Salient detection based on all convolutional feature combination[J]. Laser & Optoelectronics Progress, 55, 101502(2018).

[19] He K M, Zhang X Y, Ren S Q et al. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. [C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 1026-1034(2015).