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Contrastive embedding-based feature generation for generalized zero-shot learning

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Abstract

This paper develops a novel feature generation framework for zero-shot learning (ZSL) to recognize both fine-grained seen and unseen classes by constructing embedding from synthesized features. The observation is that the original feature space fails to capture the discriminative information for unseen classes. We first introduce a contrastive visual-semantic embedding (CVSE) approach, which integrates contrastive learning with semantic embedding to obtain both visual and semantic discriminative information for feature generation. In case, we propose to enforce contrastive learning on both real seen class samples and synthetic unseen class samples under a contrastive semantic embedding-based feature generation framework. The synthesized unseen class features together with synthesized seen class features are transformed into embedding features and utilized during classification to reduce ambiguities among semantics. We conduct experiments on four publicly available datasets of AWA1, AWA2, CUB, aPaY, showing that our method can outperform the state-of-the-art by a large margin on most of the datasets.

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References

  1. Wang W, Zheng VW, Yu H, Miao C (2019) A survey of zero-shot learning: settings, methods, and applications. ACM Trans Intell Syst Technol (TIST) 10(2):1–37

    Google Scholar 

  2. Xian Y, Lampert CH, Schiele B, Akata Z (2018) Zero-shot learning-a comprehensive evaluation of the good, the bad and the ugly. IEEE Trans Pattern Anal Mach Intell 41(9):2251–2265

    Article  Google Scholar 

  3. Ding Z, Shao M, Fu Y (2018) Generative zero-shot learning via low-rank embedded semantic dictionary. IEEE Trans Pattern Anal Mach Intell 41(12):2861–2874

    Article  Google Scholar 

  4. Akata Z, Reed S, Walter D, Lee H, Schiele B (2015) Evaluation of output embeddings for fine-grained image classification,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2927–2936

  5. Xian Y, Schiele B, Akata Z (2017) Zero-shot learning-the good, the bad and the ugly. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4582–4591

  6. Xian Y, Lorenz T, Schiele B, Akata Z (2018) Feature generating networks for zero-shot learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5542–5551

  7. Zhu Y, Elhoseiny M, Liu B, Peng X, Elgammal A (2018) A generative adversarial approach for zero-shot learning from noisy texts. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1004–1013

  8. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Advances in neural information processing systems 27

  9. Kingma DP, Welling M (2013) Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114

  10. Xian Y, Sharma S, Schiele B, Akata Z (2019) f-vaegan-d2: A feature generating framework for any-shot learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 10275–10284

  11. Liu X, Zhang F, Hou Z, Mian L, Wang Z, Zhang J, Tang J (2021) Self-supervised learning: Generative or contrastive. IEEE Transactions on Knowledge and Data Engineering

  12. Li J, Jing M, Lu K, Zhu L, Yang Y, Huang Z (2019) Alleviating feature confusion for generative zero-shot learning. In: Proceedings of the 27th ACM International Conference on Multimedia, pp 1587–1595

  13. Lampert CH, Nickisch H, Harmeling S (2009) Learning to detect unseen object classes by between-class attribute transfer. In: 2009 IEEE conference on computer vision and pattern recognition, pp 951–958, IEEE

  14. Palatucci M, Pomerleau D, Hinton GE, Mitchell TM (2009) Zero-shot learning with semantic output codes,” Advances in neural information processing systems 22

  15. Larsen ABL, Sønderby SK, Larochelle H, Winther O (2016) Autoencoding beyond pixels using a learned similarity metric. In: International conference on machine learning, pp. 1558–1566, PMLR

  16. Zhang Z, Saligrama V (2015) Zero-shot learning via semantic similarity embedding. In: Proceedings of the IEEE international conference on computer vision, pp. 4166–4174

  17. Jaiswal A, Babu AR, Zadeh MZ, Banerjee D, Makedon F (2020) A survey on contrastive self-supervised learning. Technologies 9(1):2

    Article  Google Scholar 

  18. Wang X, Zhang R, Shen C, Kong T, Li L (2021) Dense contrastive learning for self-supervised visual pre-training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3024–3033

  19. Chen T, Kornblith S, Norouzi M, Hinton G (2020) A simple framework for contrastive learning of visual representations. In: International conference on machine learning, pp 1597–1607, PMLR

  20. Gao T, Yao X, Chen D (2021) Simcse: simple contrastive learning of sentence embeddings. arXiv preprint arXiv:2104.08821

  21. Han Z, Fu Z, Chen S, Yang J (2021) Contrastive embedding for generalized zero-shot learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2371–2381

  22. Chen T, Sun Y, Shi Y, Hong L (2017) On sampling strategies for neural network-based collaborative filtering. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 767–776

  23. Wang T, Isola P (2020) Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In: International Conference on Machine Learning, pp. 9929–9939, PMLR

  24. Arjovsky M, Chintala S, Bottou L (2017) Wasserstein gan

  25. Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, pp. 248–255, Ieee

  26. Reed S, Akata Z, Lee H, Schiele B (2016) Learning deep representations of fine-grained visual descriptions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 49–58

  27. Wah C, Branson S, Welinder P, Perona P, Belongie S (2011) The caltech-ucsd birds-200-2011 dataset

  28. Farhadi A, Endres I, Hoiem D, Forsyth D (2009) Describing objects by their attributes. In: 2009 IEEE conference on computer vision and pattern recognition, pp. 1778–1785, IEEE

  29. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778

  30. Yu R, Lu W, Lu H, Wang S, Yu J (2021) Sentence pair modeling based on semantic feature map for human interaction with iot devices. Int J Mach Learn Cybern (2)

  31. Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S (2017) Gans trained by a two time-scale update rule converge to a local nash equilibrium,” Advances in neural information processing systems 30

  32. Romera-Paredes B, Torr P (2015) An embarrassingly simple approach to zero-shot learning. In: International conference on machine learning, pp. 2152–2161, PMLR

  33. Akata Z, Perronnin F, Harchaoui Z, Schmid C (2013) Label-embedding for attribute-based classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 819–826

  34. Changpinyo S, Chao W-L, Gong B, Sha F (2016) Synthesized classifiers for zero-shot learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 5327–5336

  35. Jiang H, Wang R, Shan S, Chen X (2019) Transferable contrastive network for generalized zero-shot learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9765–9774

  36. Li J, Jing M, Lu K, Zhu L, Yang Y, Huang Z (2019) Alleviating feature confusion for generative zero-shot learning. In: Proceedings of the 27th ACM International Conference on Multimedia, pp. 1587–1595

  37. Han Z, Fu Z, Yang J (2020) Learning the redundancy-free features for generalized zero-shot object recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12865–12874

  38. Shen Y, Qin J, Huang L, Liu L, Zhu F, Shao L (2020) Invertible zero-shot recognition flows. In: European Conference on Computer Vision, pp. 614–631, Springer

  39. Narayan S, Gupta A, Khan FS, Snoek CG, Shao L (2020) Latent embedding feedback and discriminative features for zero-shot classification. In: European Conference on Computer Vision, pp. 479–495, Springer

  40. Yue Z, Wang T, Sun Q, Hua X, Zhang H (2021) Counterfactual zero-shot and open-set visual recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, virtual, June 19-25, 2021, pp 15404–15414, Computer Vision Foundation / IEEE

  41. Chen X, Li J, Lan X, Zheng N (2022) Generalized zero-shot learning via multi-modal aggregated posterior aligning neural network. IEEE Trans Multimed 24:177–187

    Article  Google Scholar 

  42. Van der Maaten L, Hinton G (2008) Visualizing data using t-sne. J Mach Learn Res 9(11)

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Acknowledgements

This work was supported in part by the Fundamental Research Funds for the Central Universities (2021ZY86) and the Natural Science Foundation of China (NSFC) (61703046).

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Authors and Affiliations

  1. Beijing Forestry University, Beijing, China

    Han Wang, Tingting Zhang & Xiaoxuan Zhang

  2. National Forestry and Grassland Administration, Beijing, China

    Han Wang

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  1. Han Wang
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  2. Tingting Zhang
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Correspondence to Han Wang.

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Wang, H., Zhang, T. & Zhang, X. Contrastive embedding-based feature generation for generalized zero-shot learning. Int. J. Mach. Learn. & Cyber. 14, 1669–1681 (2023). https://doi.org/10.1007/s13042-022-01719-z

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