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The main question we address in this paper is how to scale up visual recognition of unseen classes, also known as zero-shot learning, to tens of thousands of categories as in the ImageNet-21K benchmark.
Miller, G.A.: Wordnet: a lexical database for english. Communications of the ACM 38
1995
Earlier work this paper cites.
Cox, M.A., Cox, T.F.: Multidimensional scaling. In: Handbook of data visualization, pp. 315–347. Springer (2008)
2008
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. pp. 248–255. Ieee (2009)
2009
Earlier work this paper cites.
Welinder, P., Branson, S., Mita, T., Wah, C., Schroff, F., Belongie, S., Perona, P.: Caltech-ucsd birds 200 (2010)
2010
Earlier work this paper cites.
Patterson, G., Hays, J.: Sun attribute database: Discovering, annotating, and recognizing scene attributes. In: Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. pp. 2751–2758. IEEE (2012)
2012
Earlier work this paper cites.
Elhoseiny, M., Saleh, B., Elgammal, A.: Write a classifier: Zero-shot learning using purely textual descriptions. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 2584–2591 (2013)
2013
Earlier work this paper cites.
Frome, A., Corrado, G., Shlens, J., Bengio, S., Dean, J., Ranzato, M., Mikolov, T.: Devise: A deep visual-semantic embedding model (2013)
2013
Earlier work this paper cites.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems. pp. 3111–3119 (2013)
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). pp. 1532–1543 (2014)
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Sennrich, R., Haddow, B., Birch, A.: Neural machine translation of rare words with subword units. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (2016)
2016
Earlier work this paper cites.
Elhoseiny, M., Zhu, Y., Zhang, H., Elgammal, A.: Link the head to the” beak”: Zero shot learning from noisy text description at part precision. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 6288–6297. IEEE (2017)
2017
Cited alongside, same era.
Long, Y., Shao, L.: Describing unseen classes by exemplars: Zero-shot learning using grouped simile ensemble. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV). pp. 907–915. IEEE (2017)
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Advances in neural information processing systems. pp. 5998–6008 (2017)
2017
2020
Later among the works it cites.
Sun, Q., Liu, Y., Chen, Z., Chua, T.S., Schiele, B.: Meta-transfer learning through hard tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2020)
2020
Later among the works it cites.
Wang, Y., Yao, Q., Kwok, J.T., Ni, L.M.: Generalizing from a few examples: A survey on few-shot learning. ACM Computing Surveys (CSUR) 53
2020
Later among the works it cites.
Zhang, C., Cai, Y., Lin, G., Shen, C.: Deepemd: Few-shot image classification with differentiable earth mover’s distance and structured classifiers. in 2020 ieee. In: CVF Conference on Computer Vision and Pattern Recognition. pp. 12200–12210 (2020)
2020
Later among the works it cites.
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alphaXiv is searching for related work…
Cited alongside, same era.
Van den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv e-prints pp. arXiv–1807 (2018)
2018
Cited alongside, same era.
Veeling, B.S., Linmans, J., Winkens, J., Cohen, T., Welling, M.: Rotation equivariant cnns for digital pathology. CoRR (2018)
2018
Cited alongside, same era.
Wang, X., Ye, Y., Gupta, A.: Zero-shot recognition via semantic embeddings and knowledge graphs. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 6857–6866 (2018)
2018
Cited alongside, same era.
Xian, Y., Lampert, C.H., Schiele, B., Akata, Z.: Zero-shot learning-a comprehensive evaluation of the good, the bad and the ugly. PAMI (2018)
2018
Cited alongside, same era.
Yu, Y., Ji, Z., Fu, Y., Guo, J., Pang, Y., Zhang, Z.M.: Stacked semantics-guided attention model for fine-grained zero-shot learning. In: NeurIPS (2018)
2018
Cited alongside, same era.
Kampffmeyer, M., Chen, Y., Liang, X., Wang, H., Zhang, Y., Xing, E.P.: Rethinking knowledge graph propagation for zero-shot learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11487–11496 (2019)
2019
Cited alongside, same era.
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: International Conference on Learning Representations (2019)
2019
Cited alongside, same era.
Xie, G.S., Liu, L., Jin, X., Zhu, F., Zhang, Z., Qin, J., Yao, Y., Shao, L.: Attentive region embedding network for zero-shot learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9384–9393 (2019)
2019
Cited alongside, same era.
Chen, S., Wang, W., Xia, B., Peng, Q., You, X., Zheng, F., Shao, L.: Free: Feature refinement for generalized zero-shot learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 122–131 (2021)
2021
Later among the works it cites.
Cheng, R.: Data efficient language-supervised zero-shot recognition with optimal transport distillation (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
Skorokhodov, I., Elhoseiny, M.: Class normalization for zero-shot learning. In: International Conference on Learning Representations (2021), https://openreview.net/forum?id=7pgFL2Dkyyy
2021
Later among the works it cites.
Wang, J., Jiang, B.: Zero-shot learning via contrastive learning on dual knowledge graphs. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 885–892 (2021)
2021
Later among the works it cites.
Ye, H.J., Hu, H., Zhan, D.C.: Learning adaptive classifiers synthesis for generalized few-shot learning. International Journal of Computer Vision 129
2021
Later among the works it cites.
2021
Later among the works it cites.