The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Learning to Discover Novel Visual Categories via Deep Transfer Clustering
Open accessAbstract
We consider the problem of discovering novel object categories in an image collection. While these images are unlabelled, we also assume prior knowledge of related but different image classes. We use such prior knowledge to reduce the ambiguity of clustering, and improve the quality of the newly discovered classes. Our contributions are twofold. The first contribution is to extend Deep Embedded Clustering to a transfer learning setting; we also improve the algorithm by introducing a representation bottleneck, temporal ensembling, and consistency. The second contribution is a method to estimate the number of classes in the unlabelled data. This also transfers knowledge from the known classes, using them as probes to diagnose different choices for the number of classes in the unlabelled subset. We thoroughly evaluate our method, substantially outperforming state-of-the-art techniques in a large number of benchmarks, including ImageNet, OmniGlot, CIFAR-100, CIFAR-10, and SVHN.
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Yang, J., Zhou, K., Li, Y., & Liu, Z. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02117-4 · Cited by 884
PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering
Hyun Cho, J., Mall, U., Bala, K., & Hariharan, B. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01652 · Cited by 159
Open-world continual learning: Unifying novelty detection and continual learning
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Understanding the Challenges When 3D Semantic Segmentation Faces Class Imbalanced and OOD Data
Pan, Y., Xie, F., & Zhao, H. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3256442 · Cited by 17
Metadata
- Authors
- Han, K., Vedaldi, A., & Zisserman, A.
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 8400-8408
- OpenAlex
- W2990604239
- Cited by
- 303
- References
- 35
- FWCI
- 8.81
- Type
- conference paper
- Language
- en
- Open access
- green
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- Han, K., Vedaldi, A., & Zisserman, A. (2019). Learning to Discover Novel Visual Categories via Deep Transfer Clustering. 2019 IEEE/CVF International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2019.00849
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