Abstract
Zero-shot learning aims to recognize unseen objects. From the start of zero-shot learning research, attributes have played an important role. However, previous attribute-based methods do not fully exploit attributes and their relationships. In order to overcome these drawbacks, we propose a new framework. This framework consists of the convolutional neural network, fully connected networks and the attribute knowledge graph to make classification. This framework incorporates knowledge and is suitable for incremental learning. Also, the framework treats different attributes unequally according to their relationships, which further improves the recognition accuracy. Experiments show that this framework has achieved the comparable accuracy on the AWA2 dataset. At the same time, trainable parameters are reduced by about 100 times compared to previous attribute-based methods.
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References
Xian, Y., Lampert, C.H., Schiele, B., Akata, Z.: Zero-shot learning - a comprehensive evaluation of the good, the bad and the ugly. IEEE Trans. Pattern Anal. Mach. Intell. 41(9), 2251–2265 (2019)
Lampert, C.H., Nickisch, H., Harmeling, S.: Attribute-based classification for zero-shot visual object categorization. IEEE Trans. Pattern Anal. Mach. Intell. 36(3), 453–465 (2014)
Zeithamova, D., Schlichting, M.L., Preston, A.R.: The hippocampus and inferential reasoning: building memories to navigate future decisions. Front. Human Neurosci. 6(70), 1–14 (2012)
Zhu, Y., Xie, J., Tang, Z., et al.: Semantic-guided multi-attention localization for zero-shot learning. arXiv preprint arXiv:1903.00502 (2019)
Guo, Y., Ding, G., Han, J., et al.: Zero-shot learning with attribute selection. In: AAAI, pp. 6870–6877 (2018)
Xie, G.S., Liu, L., Jin, X., et al.: Attentive region embedding network for zero-shot learning. In: CVPR, pp. 9384–9393 (2019)
Liu, Y., Guo, J., Cai, D., et al.: Attribute attention for semantic disambiguation in zero-shot learning. In: CVPR, pp. 6698–6707 (2019)
Frome, A., Corrado, G., Shlens, J., et al.: Devise: A deep visual-semantic embedding model. In: NIPS, pp. 2121–2129 (2013)
Kodirov, E., Xiang, T., Gong, S.: Semantic autoencoder for zero-shot learning. In: CVPR, pp. 3174–3183 (2017)
Akata Z, Perronnin F, Harchaoui Z., et al.: Label-embedding for attribute-based classification. In CVPR, pp. 819–826 (2013)
Akata, Z., Reed, S., Walter, D., et al.: Evaluation of output embeddings for finegrained image classification. In CVPR, pp. 2927–2936 (2015)
Romera-Paredes, B., Torr, P.: An embarrassingly simple approach to zero-shot learning. In PMLR, pp. 2152–2161 (2015)
Lin, M., Chen, Q., Yan, S.: Network in network. arXiv preprint arXiv:1312.4400 (2013)
Kaiming, H., Xiangyu, Z., Shaoqing, R., Jian, S.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)
Deng, J., Dong, W., Socher, R., et al.: Imagenet: a large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009)
Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: COMPSTAT 2010, pp. 177–186 (2009)
Changpinyo, S., Chao, W.L., Gong, B., et al.: Synthesized classifiers for zero-shot learning. In: CVPR, pp. 5327–5336 (2016)
Xian, Y., Akata, Z., Sharma, G., et al.: Latent embeddings for zero-shot classification. In: CVPR, pp. 69–77 (2016)
Zhang, Z., Saligrama, V.: Zero-shot learning via semantic similarity embedding. In: CVPR, pp. 4166–4174 (2015)
Acknowledgment
This work was supported in part by the National Natural Science Foundation of China under Grant 61974102.
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Zhang, Z., Liu, Q., Guo, D. (2021). Knowledge-Based Multiple Lightweight Attribute Networks for Zero-Shot Learning. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1516. Springer, Cham. https://doi.org/10.1007/978-3-030-92307-5_46
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DOI: https://doi.org/10.1007/978-3-030-92307-5_46
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