Abstract
Given the overwhelming performance of the Convolutional Neural Network (CNN) in the computer vision and machine learning community, this paper aims at investigating the effective transfer of the CNN descriptors in generic and fine-grained classification at a large scale. Our contribution consists in providing some simple yet effective methods in constructing a competitive baseline recognition system. Comprehensively, we study two facts in CNN transfer. (1) We demonstrate the advantage of using images with a properly large size as input to CNN instead of the conventionally resized one. (2) We benchmark the performance of different CNN layers improved by average/max pooling on the feature maps. Our evaluation and observation confirm that the Conv5 descriptor yields very competitive accuracy under such a pooling strategy. Following these good practices, we are capable of producing improved performance on seven image classification benchmarks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)
Sharif Razavian, A., Sullivan, J., Maki, A., Carlsson, S.: A baseline for visual instance retrieval with deep convolutional networks. In: ICLR (2015)
Zheng, L., Wang, S., Liu, Z., Tian, Q.: Packing and padding: coupled multi-index for accurate image retrieval. In: CVPR (2014) 1947–1954
Zheng, L., Wang, S., Tian, Q.: Coupled binary embedding for large-scale image retrieval. IEEE Trans. Image Process. 23(8), 3368–3380 (2014)
Wu, L., Shen, C., van den Hengel, A.: Deep linear discriminant analysis on fisher networks: a hybrid architecture for person re-identification. Pattern Recognit. 65, 238–250 (2017)
Wu, L., Wang, Y., Li, X., Gao, J.: What-and-where to match: deep spatially multiplicative integration networks for person re-identification. Pattern Recognit. 76, 727–738 (2018)
Wu, L., Shen, C., van den Hengel, A.: Deep recurrent convolutional networks for video-based person re-identification: an end-to-end approach. arXiv preprint arXiv:1606.01609 (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. IJCV 1–42 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 346–361. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10578-9_23
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR (2014)
Razavian, A.S., Azizpour, H., Sullivan, J., Carlsson, S.: CNN features off-the-shelf: an astounding baseline for recognition. In: CVPR Workshops (2014)
Zheng, L., Wang, S., Wang, J., Tian, Q.: Accurate image search with multi-scale contextual evidences. Int. J. Comput. Vis. 120(1), 1–13 (2016)
Babenko, A., Slesarev, A., Chigorin, A., Lempitsky, V.: Neural codes for image retrieval. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 584–599. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_38
Zheng, L., Yang, Y., Tian, Q.: SIFT meets CNN: a decade survey of instance retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 40(5), 1224–1244 (2017)
Mousavian, A., Kosecka, J.: Deep convolutional features for image based retrieval and scene categorization. arXiv preprint arXiv:1509.06033 (2015)
Tolias, G., Sicre, R., Jégou, H.: Particular object retrieval with integral max-pooling of CNN activations. arXiv preprint arXiv:1511.05879 (2015)
Fei-Fei, L., Fergus, R., Perona, P.: One-shot learning of object categories. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 594–611 (2006)
Xu, Z., Yang, Y., Hauptmann, A.G.: A discriminative CNN video representation for event detection. In: CVPR (2015)
Jégou, H., Douze, M., Schmid, C.: Product quantization for nearest neighbor search. TPAMI 33(1), 117–128 (2011)
Ng, J., Yang, F., Davis, L.: Exploiting local features from deep networks for image retrieval. In: CVPR Workshops (2015)
Szegedy, C., et al.: Going deeper with convolutions. arXiv preprint arXiv:1409.4842 (2014)
Xie, L., Zheng, L., Wang, J., Yuille, A., Tian, Q.: Interactive: inter-layer activeness propagation. arXiv preprint arXiv:1605.00052 (2016)
Zhang, N., Donahue, J., Girshick, R., Darrell, T.: Part-based R-CNNs for fine-grained category detection. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 834–849. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_54
Xiao, J., Hays, J., Ehinger, K., Oliva, A., Torralba, A., et al.: Sun database: large-scale scene recognition from abbey to zoo. In: CVPR
Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset (2007)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)
Quattoni, A., Torralba, A.: Recognizing indoor scenes. In: CVPR (2009)
Nilsback, M.E., Zisserman, A.: Automated flower classification over a large number of classes. In: Sixth Indian Conference on Computer Vision, Graphics & Image Processing (2008)
Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The caltech-UCSD birds-200-2011 dataset (2011)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR (2009)
Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: ACM Multimedia (2014)
Relja, A., Zisserman, A.: Three things everyone should know to improve object retrieval. In: CVPR (2012)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53
Lowe, D.G.: Distinctive image features from scale invariant keypoints. IJCV 60(2), 91–110 (2004)
Zhang, Y., Jia, Z., Chen, T.: Image retrieval with geometry-preserving visual phrases. In: CVPR (2011)
Zheng, L., Wang, S., Tian, L., He, F., Liu, Z., Tian, Q.: Query-adaptive late fusion for image search and person re-identification. In: CVPR (2015)
Acknowledgement
This work was supported in part to Dr. Qi Tian by ARO grant W911NF-15-1-0290 and Faculty Research Gift Awards by NEC Laboratories of America and Blippar. This work was supported in part by National Science Foundation of China (NSFC) 61429201.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Zheng, L., Zhao, Y., Wang, S., Wang, J., Yang, Y., Tian, Q. (2018). On the Large-Scale Transferability of Convolutional Neural Networks. In: Ganji, M., Rashidi, L., Fung, B., Wang, C. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 11154. Springer, Cham. https://doi.org/10.1007/978-3-030-04503-6_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-04503-6_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04502-9
Online ISBN: 978-3-030-04503-6
eBook Packages: Computer ScienceComputer Science (R0)