The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
A large-scale car dataset for fine-grained categorization and verification
Abstract
This paper aims to highlight vision related tasks centered around “car”, which has been largely neglected by vision community in comparison to other objects. We show that there are still many interesting car-related problems and applications, which are not yet well explored and researched. To facilitate future car-related research, in this paper we present our on-going effort in collecting a large-scale dataset, “CompCars”, that covers not only different car views, but also their different internal and external parts, and rich attributes. Importantly, the dataset is constructed with a cross-modality nature, containing a surveillance-nature set and a web-nature set. We further demonstrate a few important applications exploiting the dataset, namely car model classification, car model verification, and attribute prediction. We also discuss specific challenges of the car-related problems and other potential applications that worth further investigations. The latest dataset can be downloaded at http://mmlab.ie.cuhk.edu.hk/ datasets/comp_cars/index.html.
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Metadata
- Authors
- Yang, L., Luo, P., Loy, C. C., & Tang, X.
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 3973-3981
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1958236864
- Cited by
- 825
- References
- 6
- FWCI
- 26.33
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Yang, L., Luo, P., Loy, C. C., & Tang, X. (2015). A large-scale car dataset for fine-grained categorization and verification. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2015.7299023
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