The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Large-Scale Long-Tailed Recognition in an Open World
Open accessAbstract
Real world data often have a long-tailed and open-ended distribution. A practical recognition system must classify among majority and minority classes, generalize from a few known instances, and acknowledge novelty upon a never seen instance. We define Open Long-Tailed Recognition (OLTR) as learning from such naturally distributed data and optimizing the classification accuracy over a balanced test set which include head, tail, and open classes. OLTR must handle imbalanced classification, few-shot learning, and open-set recognition in one integrated algorithm, whereas existing classification approaches focus only on one aspect and deliver poorly over the entire class spectrum. The key challenges are how to share visual knowledge between head and tail classes and how to reduce confusion between tail and open classes. We develop an integrated OLTR algorithm that maps an image to a feature space such that visual concepts can easily relate to each other based on a learned metric that respects the closed-world classification while acknowledging the novelty of the open world. Our so-called dynamic meta-embedding combines a direct image feature and an associated memory feature, with the feature norm indicating the familiarity to known classes. On three large-scale OLTR datasets we curate from object-centric ImageNet, scene-centric Places, and face-centric MS1M data, our method consistently outperforms the state-of-the-art. Our code, datasets, and models enable future OLTR research and are publicly available at \url{https://liuziwei7.github.io/projects/LongTail.html}.
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Cites
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“We also adopt modulated attention [52] to encourage the head and tail classes to use different sets of spatial features.”
MethodModel-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
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Cited by
Generalized Out-of-Distribution Detection: A Survey
Yang, J., Zhou, K., Li, Y., & Liu, Z. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02117-4 · Cited by 884
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Deep metric learning for few-shot image classification: A Review of recent developments
Li, X., Yang, X., Ma, Z., & Xue, J.-H. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109381 · Cited by 243
Delving into deep imbalanced regression
Yang, Y., Zha, K., Chen, Y.-C., Wang, H., & Katabi, D. (2021) · arXiv · doi:10.48550/arxiv.2102.09554 · Cited by 229
Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection
Ding, C., Pang, G., & Shen, C. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00724 · Cited by 197
Metadata
- Authors
- Liu, Z., Miao, Z., Zhan, X., Wang, J., Gong, B., & Yu, S. X.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 2532-2541
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2962933664
- Cited by
- 1,126
- References
- 12
- FWCI
- 43.07
- Type
- conference paper
- Language
- en
- Open access
- green
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