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Cited by 3,496

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

3D Object Representations for Fine-Grained Categorization

Krause, Jonathan, Stark, Michael, Deng, Jia, Fei-Fei, Li·2013·2013 IEEE International Conference on Computer Vision Workshops
DOI 10.1109/iccvw.2013.77
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Abstract

While 3D object representations are being revived in the context of multi-view object class detection and scene understanding, they have not yet attained wide-spread use in fine-grained categorization. State-of-the-art approaches achieve remarkable performance when training data is plentiful, but they are typically tied to flat, 2D representations that model objects as a collection of unconnected views, limiting their ability to generalize across viewpoints. In this paper, we therefore lift two state-of-the-art 2D object representations to 3D, on the level of both local feature appearance and location. In extensive experiments on existing and newly proposed datasets, we show our 3D object representations outperform their state-of-the-art 2D counterparts for fine-grained categorization and demonstrate their efficacy for estimating 3D geometry from images via ultra-wide baseline matching and 3D reconstruction.

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Show all 8 references

Research works printed but not linked: 29

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Metadata

Authors
Krause, J., Stark, M., Deng, J., & Fei-Fei, L.
Venue
2013 IEEE International Conference on Computer Vision Workshops
Publisher
IEEE
Published
2013-12
Pages
554-561
ISSN
2473-9936 (ISSN-L)
DOI
10.1109/iccvw.2013.77
OpenAlex
W2138011018
Cited by
3,496
References
8
FWCI
11.31
Type
conference paper
Language
en
Open access
closed

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Krause, J., Stark, M., Deng, J., & Fei-Fei, L. (2013). 3D Object Representations for Fine-Grained Categorization. 2013 IEEE International Conference on Computer Vision Workshops. https://doi.org/10.1109/iccvw.2013.77

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