The DOI matches its Crossref record; 78 of the 78 references it cites were found in a registry or an index.
Label-Embedding for Image Classification
Open accessAbstract
Attributes act as intermediate representations that enable parameter sharing between classes, a must when training data is scarce. We propose to view attribute-based image classification as a label-embedding problem: each class is embedded in the space of attribute vectors. We introduce a function that measures the compatibility between an image and a label embedding. The parameters of this function are learned on a training set of labeled samples to ensure that, given an image, the correct classes rank higher than the incorrect ones. Results on the Animals With Attributes and Caltech-UCSD-Birds datasets show that the proposed framework outperforms the standard Direct Attribute Prediction baseline in a zero-shot learning scenario. Label embedding enjoys a built-in ability to leverage alternative sources of information instead of or in addition to attributes, such as, e.g., class hierarchies or textual descriptions. Moreover, label embedding encompasses the whole range of learning settings from zero-shot learning to regular learning with a large number of labeled examples.
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Cites
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Probabilistic Latent Semantic Indexing
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Cited by
Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly
Xian, Y., Lampert, C. H., Schiele, B., & Akata, Z. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2857768 · Cited by 1,678
Self-citationZero-Shot Learning — The Good, the Bad and the Ugly
Xian, Y., Schiele, B., & Akata, Z. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.328 · Cited by 891
Self-citationLearning Deep Representations of Fine-Grained Visual Descriptions
Reed, S., Akata, Z., Lee, H., & Schiele, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.13 · Cited by 760
Self-citationGeneralized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
Schönfeld, E., Ebrahimi, S., Sinha, S., Darrell, T., & Akata, Z. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00844 · Cited by 747
Self-citationA Survey of Zero-Shot Learning: Settings, Methods, and Applications
Wang, W., Zheng, V. W., Yu, H., & Miao, C. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3293318 · Cited by 528
Metadata
- Authors
- Akata, Z., Perronnin, F., Harchaoui, Z., & Schmid, C.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2016-07-01
- Volume
- 38
- Issue
- 7
- Pages
- 1425-1438
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2171061940
- Cited by
- 770
- References
- 78
- FWCI
- 25.34
- Type
- journal article
- Language
- en
- Open access
- green
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