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The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

Part-Based R-CNNs for Fine-Grained Category Detection

Open access
Zhang, Ning, Donahue, Jeff, Girshick, Ross, Darrell, Trevor·2014·Lecture Notes in Computer Science
DOI 10.1007/978-3-319-10590-1_54
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Abstract

Semantic part localization can facilitate fine-grained categorization by explicitly isolating subtle appearance differences associated with specific object parts. Methods for pose-normalized representations have been proposed, but generally presume bounding box annotations at test time due to the difficulty of object detection. We propose a model for fine-grained categorization that overcomes these limitations by leveraging deep convolutional features computed on bottom-up region proposals. Our method learns whole-object and part detectors, enforces learned geometric constraints between them, and predicts a fine-grained category from a pose-normalized representation. Experiments on the Caltech-UCSD bird dataset confirm that our method outperforms state-of-the-art fine-grained categorization methods in an end-to-end evaluation without requiring a bounding box at test time.

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Research works printed but not linked: 35

  1. Angelova, A., Zhu, S.: Efficient object detection and segmentation for fine-grained recognition. In: CVPR (2013) · DOI
  2. Angelova, A., Zhu, S., Lin, Y.: Image segmentation for large-scale subcategory flower recognition. In: WACV (2013) · DOI
  3. Azizpour, H., Laptev, I.: Object detection using strongly-supervised deformable part models. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 836–849. Springer, Heidelberg (2012) · DOI
  4. Belhumeur, P.N., Jacobs, D., Kriegman, D., Kumar, N.: Localizing parts of faces using a consensus of exemplars. In: CVPR (2011) · DOI · Paper page
  5. Belhumeur, P.N., et al.: Searching the world’s herbaria: A system for visual identification of plant species. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 116–129. Springer, Heidelberg (2008) · DOI
  6. Belhumeur, P.N., Jacobs, D.W., Kriegman, D.J., Kumar, N.: Localizing parts of faces using a consensus of exemplars. In: CVPR (2011) · DOI · Paper page
  7. Berg, T., Belhumeur, P.N.: POOF: Part-based one-vs.-one features for fine-grained categorization, face verification, and attribute estimation. In: CVPR (2013) · DOI · Paper page
  8. Bourdev, L., Malik, J.: Poselets: Body part detectors trained using 3D human pose annotations. In: ICCV (2009), http://www.eecs.berkeley.edu/~lbourdev/poselets · DOI · Paper page
  9. Branson, S., Wah, C., Schroff, F., Babenko, B., Welinder, P., Perona, P., Belongie, S.: Visual recognition with humans in the loop. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 438–451. Springer, Heidelberg (2010) · DOI · Paper page
  10. Chai, Y., Lempitsky, V., Zisserman, A.: Symbiotic segmentation and part localization for fine-grained categorization. In: ICCV (2013) · DOI · Paper page
  11. Chai, Y., Rahtu, E., Lempitsky, V., Van Gool, L., Zisserman, A.: TriCoS: A tri-level class-discriminative co-segmentation method for image classification. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 794–807. Springer, Heidelberg (2012) · DOI
  12. Deng, J., Krause, J., Fei-Fei, L.: Fine-grained crowdsourcing for fine-grained recognition. In: CVPR (2013) · DOI
  13. Duan, K., Parkh, D., Crandall, D., Grauman, K.: Discovering localized attributes for fine-grained recognition. In: CVPR (2012)
  14. Farrell, R., Oza, O., Zhang, N., Morariu, V.I., Darrell, T., Davis, L.S.: Birdlets: Subordinate categorization using volumetric primitives and pose-normalized appearance. In: ICCV (2011) · DOI
  15. Felzenszwalb, P.F., Huttenlocher, D.: Efficient matching of pictorial structure. In: CVPR (2000)
  16. Gavves, E., Fernando, B., Snoek, C., Smeulders, A., Tuytelaars, T.: Fine-grained categorization by alignments. In: ICCV (2013) · DOI · Paper page
  17. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR (2014) · DOI · Paper page
  18. Jia, Y.: Caffe: An open source convolutional architecture for fast feature embedding (2013), http://caffe.berkeleyvision.org/ · DOI · Paper page
  19. Khosla, A., Jayadevaprakash, N., Yao, B., Fei-Fei, L.: Novel dataset for fine-grained image categorization. In: FGVC Workshop, CVPR (2011)
  20. Liu, J., Belhumeur, P.N.: Bird part localization using exemplar-based models with enforced pose and subcategory consistency. In: ICCV (2013) · DOI
  21. Liu, J., Kanazawa, A., Jacobs, D., Belhumeur, P.: Dog breed classification using part localization. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 172–185. Springer, Heidelberg (2012) · DOI · Paper page
  22. Martinez-Munoz, G., Larios, N., Mortensen, E., Zhang, W., Yamamuro, A., Paasch, R., Payet, N., Lytle, D., Shapiro, L., Todorovic, S., Moldenke, A., Dietterich, T.: Dictionary-free categorization of very similar objects via stacked evidence trees. In: CVPR (2009) · DOI
  23. Nilsback, M.E., Zisserman, A.: A visual vocabulary for flower classification. In: CVPR (2006)
  24. Parkhi, O.M., Vedaldi, A., Jawahar, C.V., Zisserman, A.: The truth about cats and dogs. In: ICCV (2011) · DOI
  25. Parkhi, O.M., Vedaldi, A., Zisserman, A., Jawahar, C.V.: Cats and dogs. In: CVPR (2012) · DOI · Paper page
  26. Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: OverFeat: Integrated recognition, localization and detection using convolutional networks. CoRR abs/1312.6229 (2013) · arXiv
  27. Sfar, A.R., Boujemaa, N., Geman, D.: Vantage feature frames for fine-grained categorization. In: CVPR (2013) · DOI
  28. Stark, M., Krause, J., Pepik, B., Meger, D., Little, J.J., Schiele, B., Koller, D.: Fine-grained categorization for 3D scene understanding. In: BMVC (2012) · DOI
  29. Uijlings, J., van de Sande, K., Gevers, T., Smeulders, A.: Selective search for object recognition. IJCV (2013) · DOI · Paper page
  30. Xie, L., Tian, Q., Hong, R., Yan, S., Zhang, B.: Hierarchical part matching for fine-grained visual categorization. In: ICCV (2013) · DOI
  31. Yang, S., Bo, L., Wang, J., Shapiro, L.: Unsupervised template learning for fine-grained object recognition. In: NIPS (2012)
  32. Yao, B., Bradski, G., Fei-Fei, L.: A codebook-free and annotation-free approach for fine-grained image categorization. In: CVPR (2012)
  33. Yao, B., Khosla, A., Fei-Fei, L.: Combining randomization and discrimination for fine-grained image categorization. In: CVPR (2011) · DOI · Paper page
  34. Zhang, N., Farrell, R., Darrell, T.: Pose pooling kernels for sub-category recognition. In: CVPR (2012)
  35. Zhang, N., Farrell, R., Iandola, F., Darrell, T.: Deformable part descriptors for fine-grained recognition and attribute prediction. In: ICCV (2013) · DOI

Cited by

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Metadata

Authors
Zhang, N., Donahue, J., Girshick, R., & Darrell, T.
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2014
Pages
834-849
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-319-10590-1_54
OpenAlex
W56385144
Cited by
1,150
References
10
FWCI
47.39
Type
conference paper
Language
en
Open access
green
Licence
www.springer.com/tdm (tdm)

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Zhang, N., Donahue, J., Girshick, R., & Darrell, T. (2014). Part-Based R-CNNs for Fine-Grained Category Detection. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-319-10590-1_54

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