Efficient mobile implementation of A CNN-based object recognition system

MM 2016 - Proceedings of the 2016 ACM Multimedia Conference

Volumn , Issue , 2016, Pages 362-366

  Yanai, Keiji ^a   Tanno, Ryosuke ^a   Okamoto, Koichi ^a  

^a UNIVERSITY OF ELECTRO COMMUNICATIONS   (Japan)

Author keywords

Android; Convolutional neural network; iOS; Mobile implementation; Network in network

Indexed keywords

CONVOLUTION; ECONOMIC AND SOCIAL EFFECTS; IOS (OPERATING SYSTEM); MOBILE DEVICES; NEURAL NETWORKS; OBJECT RECOGNITION;

ANDROID; CONVENTIONAL METHODS; CONVOLUTIONAL NEURAL NETWORK; IN NETWORKS; MOBILE APPLICATIONS; MOBILE IMPLEMENTATION; OBJECT RECOGNITION SYSTEMS; RECOGNITION METHODS;

ANDROID (OPERATING SYSTEM);

EID: 84994558928     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2964284.2967243     Document Type: Conference Paper

References (21)

1. M. Lin, Q. Chen, and S. Yan, "Network in network," in Proc. of International Conference on Learning Represenation Conference Track, 2014.
2. H. Jegou, M. Douze, and C. Schmid, "Product quantization for nearest neighbor search," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 1, pp. 117-128, 2011.
3. S. Ioffe and C. Szegedy, "Batch normalization: Accelerating deep network training by reducing internal covariate shift," in Proc. of International Conference on Machine Learning, pp. 448-456, 2015.
4. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, "Caffe: Convolutional architecture for fast feature embedding," in Proc. of ACM International Conference Multimedia, pp. 675-678, 2014.
5. R. Collobert, K. Kavukcuoglu, and C. Farabet, "Torch7: A matlab-like environment for machine learning," in Proc. of NIPS Workshop on BIGLearn, 2011.
6. V. Vanhoucke, A. Senior, and M. Z. Mao, "Improving the speed of neural networks on CPUs," in Proc. of NIPS Workshop on Deep Learning and Unsupervised Feature Learnings, 2011.
7. Y. Gong, L. Liu, M. Yang, and L. Bourdev, "Compressing deep convolutional networks using vector quantization," arXiv preprint arXiv:1412.6115, 2014.
8. Z. Yang, M. Moczulski, M. Denil, N. de Freitas, A. Smola, L. Song, and Z. Wang, "Deep fried convnets," in Proc. of IEEE International Conference on Computer Vision, pp. 1476-1483, 2015.
9. S. Han, H. Mao, and W. J. Dally, "Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding," in Proc. of International Conference on Learning Representation, 2016.
10. M. Courbariaux, Y. Bengio, and J. P. David, "BinaryConnect: Training deep neural networks with binary weights during propagations," in Advances in Neural Information Processing Systems, pp. 3123-3131, 2015.
11. J. Wu, C. Leng, Y. Wang, Q. Hu, and J. Cheng, "Quantized convolutional neural networks for mobile devices," arXiv preprint arXiv:1512.06473, 2015.
12. Y. Kawano and K. Yanai, "Foodcam: A real-time food recognition system on a smartphone," Multimedia Tools and Applications, vol. 74, no. 14, pp. 5263-5287, 2015.
13. F. Perronnin, J. Sffanchez, and T. Mensink, "Improving the fisher kernel for large-scale image classification," in Proc. of European Conference on Computer Vision, 2010.
14. Y. Kawano and K. Yanai, "Ilsvrc on a smartphone," IPSJ Transactions on Computer Vision and Applications, vol. 6, pp. 83-87, 2014.
15. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," in Advances in Neural Information Processing Systems, 2012.
16. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, "Going deeper with convolutions," in Proc. of IEEE Computer Vision and Pattern Recognition, pp. 1-9, 2015.
17. K. Simonyan, A. Vedaldi, and A. Zisserman, "Deep inside convolutional networks: Visualising image classification models and saliency maps," in Proc. of International Conference on Learning Represenation Workshop Track, 2014.
18. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, "Dropout: A simple way to prevent neural networks from overfitting," Journal of Machine Learning Research, vol. 15, no. 1, pp. 1929-1958, 2014.
19. Y. Matsuda, H. Hoashi, and K. Yanai, "Recognition of multiple-food images by detecting candidate regions," in Proc. of IEEE International Conference on Multimedia and Expo, pp. 1554-1564, 2012.
20. K. Yanai and Y. Kawano, "Food image recognition using deep convolutional network with pre-training and fine-tuining," in Proc. of ICME Workshop on Multimedia for Cooking and eating Activities (CEA), 2015.
21. J. Johnson, A. Alahi, and L. Fei-Fei, "Perceptual losses for real-time style transfer and super-resolution," arXiv preprint arXiv:1603.08155, 2016.