The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Deformable Part Descriptors for Fine-Grained Recognition and Attribute Prediction
Abstract
Recognizing objects in fine-grained domains can be extremely challenging due to the subtle differences between subcategories. Discriminative markings are often highly localized, leading traditional object recognition approaches to struggle with the large pose variation often present in these domains. Pose-normalization seeks to align training exemplars, either piecewise by part or globally for the whole object, effectively factoring out differences in pose and in viewing angle. Prior approaches relied on computationally-expensive filter ensembles for part localization and required extensive supervision. This paper proposes two pose-normalized descriptors based on computationally-efficient deformable part models. The first leverages the semantics inherent in strongly-supervised DPM parts. The second exploits weak semantic annotations to learn cross-component correspondences, computing pose-normalized descriptors from the latent parts of a weakly-supervised DPM. These representations enable pooling across pose and viewpoint, in turn facilitating tasks such as fine-grained recognition and attribute prediction. Experiments conducted on the Caltech-UCSD Birds 200 dataset and Berkeley Human Attribute dataset demonstrate significant improvements of our approach over state-of-art algorithms.
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Cites
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
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
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
Self-citationAutomated 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
Describing objects by their attributes
Farhadi, A., Endres, I., Hoiem, D., & Forsyth, D. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206772 · Cited by 1,917
The Representation and Matching of Pictorial Structures
Fischler, M. A., & Elschlager, R. A. (1973) · IEEE Transactions on Computers · doi:10.1109/t-c.1973.223602 · Cited by 1,322
Ensemble of exemplar-SVMs for object detection and beyond
Malisiewicz, T., Gupta, A., & Efros, A. A. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126229 · Cited by 865
Research works printed but not linked: 40
- duan, 2012, Discovering localized attributes for fine-grained recognition, CVPR
- 10.1109/ICCV.2011.6126281 · DOI · Paper page
- 10.1109/CVPR.2013.81 · DOI · Paper page
- 10.1109/ICCV.2011.6126398 · DOI
- 10.1109/CVPR.2006.42 · DOI · Paper page
- deng, 2010, What does classifying more than 10, 000 image categories tell us?, ECCV
- 10.1109/CVPR.2013.113 · DOI
- branson, 2010, Visual recognition with humans in the loop, ECCV
- chai, 2012, Tricos: A trilevel class-discriminative co-segmentation method for image classification, ECCV
- parkhi, 2012, Cats and dogs, CVPR
- bourdev, 2009, Poselets: Body part detectors trained using 3D human pose annotations, ICCV
- rother, 2004, Grabcut-interactive foreground extraction using iterated graph cuts, SIGGRAPH, 10.1145/1015706.1015720 · DOI · Paper page
- 10.1109/ICCV.2011.6126450 · DOI
- 10.1007/s11263-009-0275-4 · DOI · Paper page
- dubout, 2012, Exact acceleration of linear object detectors, ECCV
- yang, 2012, Unsupervised template learning for fine-grained object recognition, NIPS
- welinder, 2010, Caltech-UCSD Birds 200
- 10.1109/ICCV.2011.6126309 · DOI · Paper page
- 10.5244/C.26.36 · DOI
- yao, 2012, A codebook-free and annotation-free approach for fine-grained image categorization, CVPR
- 10.1109/CVPR.2011.5995741 · DOI · Paper page
- zhang, 2012, Pose pooling kernels for sub-category recognition, CVPR
- 10.1109/CVPR.2011.5995368 · DOI · Paper page
- 10.1109/ICCV.2011.6126238 · DOI · Paper page
- khosla, 2011, Novel dataset for finegrained image categorization, CVPR Workshop on FGVC
- 10.1109/ICCV.2009.5459250 · DOI · Paper page
- 10.1109/CVPR.2009.5206594 · DOI · Paper page
- liu, 2012, Dog breed classification using part localization, ECCV
- azizpour, 2012, Object detection using strongly-supervised deformable part models, ECCV
- 10.1109/WACV.2013.6474997 · DOI
- 10.1109/ICCV.2011.6126413 · DOI · Paper page
- 10.1109/CVPR.2013.110 · DOI · Paper page
- maji, 2013, Fine-grained visual classification of aircraft, Technical Report
- berg, 2010, Automatic attribute discovery and characterization from noisy Web data, ECCV
- 10.1109/CVPR.2013.128 · DOI · Paper page
- 10.1109/CVPR.2009.5206574 · DOI
- 10.1109/CVPR.2011.5995602 · DOI · Paper page
- belhumeur, 2008, Searching the worlds herbaria: A system for visual identification of plant Species, ECCV
- bourdev, 2010, Detecting people using mutually consistent poselet activations, ECCV
- bo, 2010, Kernel descriptors for visual recognition, NIPS
Cited by
Learning Deep Features for Discriminative Localization
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.319 · Cited by 11,428
CNN Features Off-the-Shelf: An Astounding Baseline for Recognition
Razavian, A. S., Azizpour, H., Sullivan, J., & Carlsson, S. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2014.131 · Cited by 4,451
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
Self-citationPart-Based R-CNNs for Fine-Grained Category Detection
Zhang, N., Donahue, J., Girshick, R., & Darrell, T. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_54 · Cited by 1,150
Self-citationMulti-attentional Deepfake Detection
Zhao, H., Wei, T., Zhou, W., Zhang, W., Chen, D., & Yu, N. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00222 · Cited by 1,019
Metadata
- Authors
- Zhang, N., Farrell, R., Iandola, F., & Darrell, T.
- Venue
- 2013 IEEE International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2013-12
- Pages
- 729-736
- OpenAlex
- W2079789819
- Cited by
- 193
- References
- 7
- FWCI
- 19.20
- Type
- conference paper
- Language
- en
- Open access
- closed
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