The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Part-Stacked CNN for Fine-Grained Visual Categorization
Open accessAbstract
In the context of fine-grained visual categorization, the ability to interpret models as human-understandable visual manuals is sometimes as important as achieving high classification accuracy. In this paper, we propose a novel Part-Stacked CNN architecture that explicitly explains the finegrained recognition process by modeling subtle differences from object parts. Based on manually-labeled strong part annotations, the proposed architecture consists of a fully convolutional network to locate multiple object parts and a two-stream classification network that encodes object-level and part-level cues simultaneously. By adopting a set of sharing strategies between the computation of multiple object parts, the proposed architecture is very efficient running at 20 frames/sec during inference. Experimental results on the CUB-200-2011 dataset reveal the effectiveness of the proposed architecture, from multiple perspectives of classification accuracy, model interpretability, and efficiency. Being able to provide interpretable recognition results in realtime, the proposed method is believed to be effective in practical applications.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
Basic objects in natural categories
Rosch, E., Mervis, C. B., Gray, W. D., Johnson, D. M., & Boyes-Braem, P. (1976) · Cognitive Psychology · doi:10.1016/0010-0285(76)90013-x · Cited by 5,643
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2013) · arXiv · doi:10.48550/arxiv.1310.1531 · Cited by 4,019
Automated Flower Classification over a Large Number of Classes
Nilsback, M.-E., & Zisserman, A. (2008) · 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing · doi:10.1109/icvgip.2008.47 · Cited by 3,442
Going Deeper with Convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2014) · arXiv · doi:10.48550/arxiv.1409.4842 · Cited by 1,461
CNN Features off-the-shelf: an Astounding Baseline for Recognition
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PANDA: Pose Aligned Networks for Deep Attribute Modeling
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Cited by
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Zheng, H., Fu, J., Mei, T., & Luo, J. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.557 · Cited by 1,045
This Looks Like That: Deep Learning for Interpretable Image Recognition
Chen, C., Li, O., Tao, C., Barnett, A. J., Su, J., & Rudin, C. (2018) · arXiv · doi:10.48550/arxiv.1806.10574 · Cited by 702
Low-Rank Bilinear Pooling for Fine-Grained Classification
Kong, S., & Fowlkes, C. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.743 · Cited by 367
A survey on deep learning-based fine-grained object classification and semantic segmentation
Zhao, B., Feng, J., Wu, X., & Yan, S. (2017) · International Journal of Automation and Computing · doi:10.1007/s11633-017-1053-3 · Cited by 339
Presence-Only Geographical Priors for Fine-Grained Image Classification
Aodha, O. M., Cole, E., & Perona, P. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00969 · Cited by 149
Metadata
- Authors
- Huang, S., Xu, Z., Tao, D., & Zhang, Y.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 1173-1182
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2202499615
- Cited by
- 468
- References
- 8
- FWCI
- 16.92
- Type
- conference paper
- Language
- en
- Open access
- green
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- Huang, S., Xu, Z., Tao, D., & Zhang, Y. (2016). Part-Stacked CNN for Fine-Grained Visual Categorization. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2016.132
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