The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Fine-Grained Visual Comparisons with Local Learning
Abstract
Given two images, we want to predict which exhibits a particular visual attribute more than the other-even when the two images are quite similar. Existing relative attribute methods rely on global ranking functions; yet rarely will the visual cues relevant to a comparison be constant for all data, nor will humans' perception of the attribute necessarily permit a global ordering. To address these issues, we propose a local learning approach for fine-grained visual comparisons. Given a novel pair of images, we learn a local ranking model on the fly, using only analogous training comparisons. We show how to identify these analogous pairs using learned metrics. With results on three challenging datasets-including a large newly curated dataset for fine-grained comparisons-our method outperforms stateof-the-art methods for relative attribute prediction.
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Metadata
- Authors
- Yu, A., & Grauman, K.
- Venue
- 2014 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2014-06
- Pages
- 192-199
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2093848332
- Cited by
- 454
- References
- 30
- FWCI
- 9.19
- Type
- conference paper
- Language
- en
- Open access
- closed
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