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Relative attributes
Abstract
Human-nameable visual “attributes” can benefit various recognition tasks. However, existing techniques restrict these properties to categorical labels (for example, a person is `smiling' or not, a scene is `dry' or not), and thus fail to capture more general semantic relationships. We propose to model relative attributes. Given training data stating how object/scene categories relate according to different attributes, we learn a ranking function per attribute. The learned ranking functions predict the relative strength of each property in novel images. We then build a generative model over the joint space of attribute ranking outputs, and propose a novel form of zero-shot learning in which the supervisor relates the unseen object category to previously seen objects via attributes (for example, `bears are furrier than giraffes'). We further show how the proposed relative attributes enable richer textual descriptions for new images, which in practice are more precise for human interpretation. We demonstrate the approach on datasets of faces and natural scenes, and show its clear advantages over traditional binary attribute prediction for these new tasks.
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Cited by
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Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., & Lee, H. (2016) · arXiv · doi:10.48550/arxiv.1605.05396 · Cited by 2,494
Image retrieval using scene graphs
Johnson, J., Krishna, R., Stark, M., Li, L.-J., Shamma, D. A., Bernstein, M. S., & Fei-Fei, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298990 · Cited by 1,101
An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild
Chao, W.-L., Changpinyo, S., Gong, B., & Sha, F. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_4 · Cited by 616
A 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
Transductive Multi-View Zero-Shot Learning
Fu, Y., Hospedales, T. M., Xiang, T., & Gong, S. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2408354 · Cited by 514
PANDA: Pose Aligned Networks for Deep Attribute Modeling
Zhang, N., Paluri, M., Ranzato, M., Darrell, T., & Bourdev, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.212 · Cited by 495
A Joint Model of Language and Perception for Grounded Attribute Learning
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Iterative Visual Reasoning Beyond Convolutions
Chen, X., Li, L.-J., Fei-Fei, L., & Gupta, A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00756 · Cited by 236
Hendricks, L. A., Hu, R., Darrell, T., & Akata, Z. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01216-8_17 · Cited by 171
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Learning Semantic Deformation Flows with 3D Convolutional Networks
Yumer, M. E., & Mitra, N. J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46466-4_18 · Cited by 60
We Don’t Need No Bounding-Boxes: Training Object Class Detectors Using Only Human Verification
Papadopoulos, D. P., Uijlings, J. R. R., Keller, F., & Ferrari, V. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.99 · Cited by 21
Metadata
- Authors
- Parikh, D., & Grauman, K.
- Venue
- 2011 International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2011-11
- Pages
- 503-510
- OpenAlex
- W2294130536
- Cited by
- 851
- References
- 0
- FWCI
- 49.49
- Type
- conference paper
- Language
- en
- Open access
- closed
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