Spatial pyramid pooling in deep convolutional networks for visual recognition

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8691 LNCS, Issue PART 3, 2014, Pages 346-361

  He, Kaiming ^a   Zhang, Xiangyu ^b   Ren, Shaoqing ^c   Sun, Jian ^a  

^a MICROSOFT RESEARCH   (United States)

^b XI'AN JIAOTONG UNIVERSITY   (China)

^c UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CONVOLUTION; FACE RECOGNITION; NEURAL NETWORKS;

CLASSIFICATION METHODS; CONVOLUTIONAL NETWORKS; CONVOLUTIONAL NEURAL NETWORK; NETWORK STRUCTURES; OBJECT DETECTION; RECOGNITION ACCURACY; SPATIAL PYRAMIDS; VISUAL RECOGNITION;

IMAGE CLASSIFICATION;

EID: 84906508687     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-10578-9_23     Document Type: Conference Paper

References (33)

1. Chang, C.C., Lin, C.J.: Libsvm: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, TIST (2011)
2. Chatfield, K., Lempitsky, V., Vedaldi, A., Zisserman, A.: The devil is in the details: An evaluation of recent feature encoding methods. In: BMVC (2011)
3. Cheng, M.M., Zhang, Z., Lin, W.Y., Torr, P.: BING: Binarized normed gradients for objectness estimation at 300fps. In: CVPR (2014)
4. Coates, A., Ng, A.: The importance of encoding versus training with sparse coding and vector quantization. In: ICML (2011)
5. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)
6. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR (2009)
7. Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., Darrell, T.: Decaf: A deep convolutional activation feature for generic visual recognition. ArXiv:1310.1531 (2013)
8. Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge, VOC 2007 Results (2007)
9. Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. CVIU (2007)
10. Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. PAMI (2010)
11. van Gemert, J.C., Geusebroek, J.-M., Veenman, C.J., Smeulders, A.W.M.: Kernel codebooks for scene categorization. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 696-709. Springer, Heidelberg (2008)
12. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR (2014)
13. Gong, Y., Wang, L., Guo, R., Lazebnik, S.: Multi-scale orderless pooling of deep convolutional activation features. ArXiv:1403.1840 (2014)
14. Grauman, K., Darrell, T.: The pyramid match kernel: Discriminative classification with sets of image features. In: ICCV (2005)
15. Howard, A.G.: Some improvements on deep convolutional neural network based image classification. ArXiv:1312.5402 (2013)
16. Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)
17. Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: CVPR (2006)
18. LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Backpropagation applied to handwritten zip code recognition. Neural Computation (1989)
19. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV (2004)
20. Oquab, M., Bottou, L., Laptev, I., Sivic, J., et al.: Learning and transferring midlevel image representations using convolutional neural networks. In: CVPR (2014)
21. Perronnin, F., Sánchez, J., Mensink, T.: Improving the fisher kernel for large-scale image classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 143-156. Springer, Heidelberg (2010)
22. Razavian, A.S., Azizpour, H., Sullivan, J., Carlsson, S.: Cnn features off-the-shelf: An astounding baseline for recogniton. In: CVPR 2014, DeepVision Workshop (2014)
23. van de Sande, K.E., Uijlings, J.R., Gevers, T., Smeulders, A.W.: Segmentation as selective search for object recognition. In: ICCV (2011)
24. Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: Overfeat: Integrated recognition, localization and detection using convolutional networks. ArXiv:1312.6229 (2013)
25. Sivic, J., Zisserman, A.: Video google: A text retrieval approach to object matching in videos. In: ICCV (2003)
26. Szegedy, C., Toshev, A., Erhan, D.: Deep neural networks for object detection. In: NIPS (2013)
27. Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: Deepface: Closing the gap to human-level performance in face verification. In: CVPR (2014)
28. Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: Locality-constrained linear coding for image classification. In: CVPR (2010)
29. Wang, X., Yang, M., Zhu, S., Lin, Y.: Regionlets for generic object detection. In: ICCV (2013)
30. Yang, J., Yu, K., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: CVPR (2009)
31. Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional neural networks. ArXiv:1311.2901 (2013)
32. Zhang, N., Paluri, M., Ranzato, M., Darrell, T., Bourdevr, L.: Panda: Pose aligned networks for deep attribute modeling. In: CVPR (2014)
33. Zou, W.Y., Wang, X., Sun, M., Lin, Y.: Generic object detection with dense neural patterns and regionlets. ArXiv:1404.4316 (2014)