{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T03:44:28Z","timestamp":1762055068147,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T00:00:00Z","timestamp":1655337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Zero-Shot Learning (ZSL) is related to training machine learning models capable of classifying or predicting classes (labels) that are not involved in the training set (unseen classes). A well-known problem in Deep Learning (DL) is the requirement for large amount of training data. Zero-Shot learning is a straightforward approach that can be applied to overcome this problem. We propose a Hybrid Feature Model (HFM) based on conditional autoencoders for training a classical machine learning model on pseudo training data generated by two conditional autoencoders (given the semantic space as a condition): (a) the first autoencoder is trained with the visual space concatenated with the semantic space and (b) the second autoencoder is trained with the visual space as an input. Then, the decoders of both autoencoders are fed by the test data of the unseen classes to generate pseudo training data. To classify the unseen classes, the pseudo training data are combined to train a support vector machine. Tests on four different benchmark datasets show that the proposed method shows promising results compared to the current state-of-the-art when it comes to settings for both standard Zero-Shot Learning (ZSL) and Generalized Zero-Shot Learning (GZSL).<\/jats:p>","DOI":"10.3390\/jimaging8060171","type":"journal-article","created":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T01:48:12Z","timestamp":1655430492000},"page":"171","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["HFM: A Hybrid Feature Model Based on Conditional Auto Encoders for Zero-Shot Learning"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1239-9261","authenticated-orcid":false,"given":"Fadi","family":"Al Machot","sequence":"first","affiliation":[{"name":"Faculty of Science and Technology, Norwegian University of Life Science (NMBU), 1430 \u00c5s, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohib","family":"Ullah","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Norwegian University of Science and Technology, 2819 Gj\u00f8vik, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Habib","family":"Ullah","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology, Norwegian University of Life Science (NMBU), 1430 \u00c5s, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yan, T., Li, H., Sun, B., Wang, Z., and Luo, Z. (2022, February 02). Discriminative Feature Mining and Enhancement Network for Low-resolution Fine-grained Image Recognition. IEEE Trans. Circuits Syst. Video Technol., Available online: https:\/\/ieeexplore.ieee.org\/document\/9684445.","DOI":"10.1109\/TCSVT.2022.3144186"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Shagdar, Z., Ullah, M., Ullah, H., and Cheikh, F.A. (2021, January 23\u201325). Geometric Deep Learning for Multi-Object Tracking: A Brief Review. Proceedings of the 2021 9th European Workshop on Visual Information Processing (EUVIP), Paris, France.","DOI":"10.1109\/EUVIP50544.2021.9484040"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104059","DOI":"10.1016\/j.autcon.2021.104059","article-title":"Natural language processing for smart construction: Current status and future directions","volume":"134","author":"Wu","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ullah, H., Ahmed, T.U., Ullah, M., and Cheikh, F.A. (2021, January 19\u201322). IR-SSL: Improved Regularization Based Semi-Supervised Learning For Land Cover Classification. Proceedings of the 2021 IEEE International Conference on Image Processing (ICIP), Anchorage, AK, USA.","DOI":"10.1109\/ICIP42928.2021.9506681"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"73327","DOI":"10.1109\/ACCESS.2021.3081050","article-title":"IA-SSLM: Irregularity-Aware Semi-Supervised Deep Learning Model for Analyzing Unusual Events in Crowds","volume":"9","author":"Aljaloud","year":"2021","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.neunet.2022.02.001","article-title":"Deep Bayesian Unsupervised Lifelong Learning","volume":"149","author":"Zhao","year":"2022","journal-title":"Neural Netw."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hunter, R.A., Pompano, R.R., and Tuchler, M.F. (2022). Alternative Assessment of Active Learning. Active Learning in the Analytical Chemistry Curriculum, ACS Publications.","DOI":"10.1021\/bk-2022-1409.ch015"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1037\/0033-295X.94.2.115","article-title":"Recognition-by-components: A theory of human image understanding","volume":"94","author":"Biederman","year":"1987","journal-title":"Psychol. Rev."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Min, S., Yao, H., Xie, H., Wang, C., Zha, Z.J., and Zhang, Y. (2020, January 13\u201319). Domain-aware visual bias eliminating for generalized zero-shot learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01268"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Han, Z., Fu, Z., Chen, S., and Yang, J. (2021, January 20\u201325). Contrastive embedding for generalized zero-shot learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00240"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"108469","DOI":"10.1016\/j.patcog.2021.108469","article-title":"A zero-shot learning framework via cluster-prototype matching","volume":"124","author":"Zhang","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1109\/TPAMI.2013.140","article-title":"Attribute-based classification for zero-shot visual object categorization","volume":"36","author":"Lampert","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_13","unstructured":"Norouzi, M., Mikolov, T., Bengio, S., Singer, Y., Shlens, J., Frome, A., Corrado, G.S., and Dean, J. (2013). Zero-shot learning by convex combination of semantic embeddings. arXiv."},{"key":"ref_14","unstructured":"Gao, R., Hou, X., Qin, J., Shen, Y., Long, Y., Liu, L., Zhang, Z., and Shao, L. (2022, February 02). Visual-Semantic Aligned Bidirectional Network for Zero-Shot Learning. IEEE Trans. Multimed., Available online: https:\/\/ieeexplore.ieee.org\/document\/9693152."},{"key":"ref_15","first-page":"1","article-title":"Devise: A deep visual-semantic embedding model","volume":"26","author":"Frome","year":"2013","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_16","unstructured":"Annadani, Y., and Biswas, S. (2018, January 18\u201323). Preserving semantic relations for zero-shot learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Vyas, M.R., Venkateswara, H., and Panchanathan, S. (2020, January 23\u201328). Leveraging Seen and Unseen Semantic Relationships for Generative Zero-Shot Learning. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58577-8_5"},{"key":"ref_18","first-page":"1","article-title":"Zero-shot learning through cross-modal transfer","volume":"26","author":"Socher","year":"2013","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_19","unstructured":"Zhang, L., Sung, F., Liu, F., Xiang, T., Gong, S., Yang, Y., and Hospedales, T.M. (2017). Actor-critic sequence training for image captioning. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Akata, Z., Reed, S., Walter, D., Lee, H., and Schiele, B. (2015, January 7\u201312). Evaluation of output embeddings for fine-grained image classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298911"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Xian, Y., Akata, Z., Sharma, G., Nguyen, Q., Hein, M., and Schiele, B. (2016, January 27\u201330). Latent embeddings for zero-shot classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.15"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Romera-Paredes, B., and Torr, P. (2015, January 6\u201311). An embarrassingly simple approach to zero-shot learning. Proceedings of the International Conference on Machine Learning, Lille, France.","DOI":"10.1007\/978-3-319-50077-5_2"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1109\/TPAMI.2015.2487986","article-title":"Label-embedding for image classification","volume":"38","author":"Akata","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, L., Xiang, T., and Gong, S. (2017, January 21\u201326). Learning a deep embedding model for zero-shot learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.321"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Xian, Y., Sharma, S., Schiele, B., and Akata, Z. (2019, January 15\u201320). f-vaegan-d2: A feature generating framework for any-shot learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01052"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Narayan, S., Gupta, A., Khan, F.S., Snoek, C.G., and Shao, L. (2020, January 23\u201328). Latent embedding feedback and discriminative features for zero-shot classification. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58542-6_29"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Mishra, A., Krishna Reddy, S., Mittal, A., and Murthy, H.A. (2018, January 18\u201322). A generative model for zero shot learning using conditional variational autoencoders. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00294"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Changpinyo, S., Chao, W.L., Gong, B., and Sha, F. (2016, January 27\u201330). Synthesized classifiers for zero-shot learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.575"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kodirov, E., Xiang, T., and Gong, S. (2017, January 21\u201326). Semantic autoencoder for zero-shot learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.473"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H., and Hospedales, T.M. (2018, January 18\u201322). Learning to compare: Relation network for few-shot learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00131"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"127493","DOI":"10.1016\/j.jhydrol.2022.127493","article-title":"Stochastic simulation of deltas based on a concurrent multi-stage VAE-GAN model","volume":"607","author":"Zhang","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_32","unstructured":"Kingma, D.P., and Welling, M. (2013). Auto-encoding variational bayes. arXiv."},{"key":"ref_33","first-page":"1","article-title":"Learning structured output representation using deep conditional generative models","volume":"28","author":"Sohn","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Bowman, S.R., Vilnis, L., Vinyals, O., Dai, A.M., Jozefowicz, R., and Bengio, S. (2015). Generating sentences from a continuous space. arXiv.","DOI":"10.18653\/v1\/K16-1002"},{"key":"ref_35","unstructured":"Zhao, S., Song, J., and Ermon, S. (2017). Towards deeper understanding of variational autoencoding models. arXiv."},{"key":"ref_36","first-page":"1","article-title":"Isolating sources of disentanglement in variational autoencoders","volume":"31","author":"Chen","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Patterson, G., and Hays, J. (2012, January 16\u201321). Sun attribute database: Discovering, annotating, and recognizing scene attributes. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6247998"},{"key":"ref_38","unstructured":"Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S. (2011). The Caltech-Ucsd Birds-200-2011 Dataset, California Institute of Technology."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Xian, Y., Schiele, B., and Akata, Z. (2017, January 211\u201326). Zero-shot learning-the good, the bad and the ugly. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.328"},{"key":"ref_40","unstructured":"Bursztein, E., Chollet, F., Jin, H., Watson, M., and Zhu, Q.S. (2022, February 02). Keras: The Python Deep Learning API. Available online: https:\/\/keras.io."},{"key":"ref_41","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2022, February 02). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Available online: tensorflow.org."},{"key":"ref_42","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Chao, W.L., Changpinyo, S., Gong, B., and Sha, F. (2016, January 8\u201316). An empirical study and analysis of generalized zero-shot learning for object recognition in the wild. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46475-6_4"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Xian, Y., Lorenz, T., Schiele, B., and Akata, Z. (2018, January 18\u201322). Feature generating networks for zero-shot learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00581"},{"key":"ref_45","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Liu, B., Dong, Q., and Hu, Z. (2020, January 1\u201312). Zero-shot learning from adversarial feature residual to compact visual feature. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i07.6821"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhou, L., Bai, X., Huang, Y., Gu, L., Zhou, J., and Harada, T. (2021, January 20\u201325). Goal-oriented gaze estimation for zero-shot learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00379"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/8\/6\/171\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:32:44Z","timestamp":1760139164000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/8\/6\/171"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,16]]},"references-count":47,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["jimaging8060171"],"URL":"https:\/\/doi.org\/10.3390\/jimaging8060171","relation":{},"ISSN":["2313-433X"],"issn-type":[{"type":"electronic","value":"2313-433X"}],"subject":[],"published":{"date-parts":[[2022,6,16]]}}}