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Label hierarchies are often available apriori as part of biological taxonomy or language datasets WordNet.
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2017
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Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7263–7271 (2017)
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Reddi, S.J., Kale, S., Kumar, S.: On the convergence of adam and beyond. In: International Conference on Learning Representations (2018), https://openreview.net/forum?id=ryQu7f-RZ
2018
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Bertinetto, L., Mueller, R., Tertikas, K., Samangooei, S., Lord, N.A.: Making better mistakes: Leveraging class hierarchies with deep networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2020)
2020
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Liu, S., Chen, J., Pan, L., Ngo, C.W., Chua, T.S., Jiang, Y.G.: Hyperbolic visual embedding learning for zero-shot recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9273–9281 (2020)
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Yuan, L., Tay, F.E., Li, G., Wang, T., Feng, J.: Revisiting knowledge distillation via label smoothing regularization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2020)
2020
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Chang, D., Pang, K., Zheng, Y., Ma, Z., Song, Y.Z., Guo, J.: Your “flamingo” is my “bird”: Fine-grained, or not. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11476–11485 (2021)
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2018
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Van Horn, G., Mac Aodha, O., Song, Y., Cui, Y., Sun, C., Shepard, A., Adam, H., Perona, P., Belongie, S.: The inaturalist species classification and detection dataset. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 8769–8778 (2018)
2018
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Wehrmann, J., Cerri, R., Barros, R.: Hierarchical multi-label classification networks. In: International Conference on Machine Learning. pp. 5075–5084. PMLR (2018)
2018
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Barz, B., Denzler, J.: Hierarchy-based image embeddings for semantic image retrieval. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). pp. 638–647. IEEE (2019)
2019
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Brust, C.A., Denzler, J.: Integrating domain knowledge: using hierarchies to improve deep classifiers. In: Asian Conference on Pattern Recognition. pp. 3–16. Springer (2019)
2019
Cited alongside, same era.
Mao, Y., Tian, J., Han, J., Ren, X.: Hierarchical text classification with reinforced label assignment. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). pp. 445–455 (2019)
2019
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2021
Later among the works it cites.
Karthik, S., Prabhu, A., Dokania, P.K., Gandhi, V.: No cost likelihood manipulation at test time for making better mistakes in deep networks. In: International Conference on Learning Representations (2021), https://openreview.net/forum?id=193sEnKY1ij
2021
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Landrieu, L., Garnot, V.S.F.: Leveraging class hierarchies with metric-guided prototype learning. In: British Machine Vision Conference (BMVC) (2021)
2021
Later among the works it cites.
Su, J., Maji, S.: Semi-supervised learning with taxonomic labels. In: British Machine Vision Conference (BMVC) (2021)
2021
Later among the works it cites.
Wang, Y., Wang, Z., Hu, Q., Zhou, Y., Su, H.: Hierarchical semantic risk minimization for large-scale classification. IEEE Transactions on Cybernetics (2021)
2021
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Garg, A., Bagga, S., Singh, Y., Anand, S.: Hiermatch: Leveraging label hierarchies for improving semi-supervised learning. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 1015–1024 (2022)
2022
Closest in time.
Yang, Z., Bastan, M., Zhu, X., Gray, D., Samaras, D.: Hierarchical proxy-based loss for deep metric learning. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 1859–1868 (2022)
2022
Closest in time.