{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T17:59:00Z","timestamp":1761674340084,"version":"build-2065373602"},"reference-count":25,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T00:00:00Z","timestamp":1668384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004829","name":"Science and Technology Department of Sichuan Province","doi-asserted-by":"publisher","award":["2022YFG0041","2021CDLZ-11","6142103190415","62262074","2022YFG0159","62072319"],"award-info":[{"award-number":["2022YFG0041","2021CDLZ-11","6142103190415","62262074","2022YFG0159","62072319"]}],"id":[{"id":"10.13039\/501100004829","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Luzhou Science and Technology Innovation R&amp;D Program","award":["2022YFG0041","2021CDLZ-11","6142103190415","62262074","2022YFG0159","62072319"],"award-info":[{"award-number":["2022YFG0041","2021CDLZ-11","6142103190415","62262074","2022YFG0159","62072319"]}]},{"name":"Foundation of Science and Technology on Communication Security Laboratory","award":["2022YFG0041","2021CDLZ-11","6142103190415","62262074","2022YFG0159","62072319"],"award-info":[{"award-number":["2022YFG0041","2021CDLZ-11","6142103190415","62262074","2022YFG0159","62072319"]}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022YFG0041","2021CDLZ-11","6142103190415","62262074","2022YFG0159","62072319"],"award-info":[{"award-number":["2022YFG0041","2021CDLZ-11","6142103190415","62262074","2022YFG0159","62072319"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004829","name":"Science and Technology Department of Sichuan Province","doi-asserted-by":"publisher","award":["2022YFG0041","2021CDLZ-11","6142103190415","62262074","2022YFG0159","62072319"],"award-info":[{"award-number":["2022YFG0041","2021CDLZ-11","6142103190415","62262074","2022YFG0159","62072319"]}],"id":[{"id":"10.13039\/501100004829","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022YFG0041","2021CDLZ-11","6142103190415","62262074","2022YFG0159","62072319"],"award-info":[{"award-number":["2022YFG0041","2021CDLZ-11","6142103190415","62262074","2022YFG0159","62072319"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the task of image instance segmentation, semi-supervised instance segmentation algorithms have received constant research attention over recent years. Among these algorithms, algorithms based on transfer learning are better than algorithms based on pseudo-label generation in terms of segmentation performance, but they can not make full use of the relevant characteristics of source tasks. To improve the accuracy of these algorithms, this work proposes a semi-supervised instance segmentation model AFT-Mask (attention-based feature transfer Mask R-CNN) based on category attention. The AFT-Mask model takes the result of object-classification prediction as \u201cattention\u201d to improve the performance of the feature-transfer module. In detail, we designed a migration-optimization module for connecting feature migration and classification prediction to enhance segmentation-prediction accuracy. To verify the validity of the AFT-Mask model, experiments were conducted on two types of datasets. Experimental results show that the AFT-Mask model can achieve effective knowledge transfer and improve the performance of the benchmark model on semi-supervised instance segmentation.<\/jats:p>","DOI":"10.3390\/s22228794","type":"journal-article","created":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T02:36:40Z","timestamp":1668479800000},"page":"8794","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Semi-Supervised Instance-Segmentation Model for Feature Transfer Based on Category Attention"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2442-302X","authenticated-orcid":false,"given":"Hao","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science, Sichuan University, Chengdu 610065, China"}]},{"given":"Juncai","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Sichuan University, Chengdu 610065, China"}]},{"given":"Changhai","family":"Huang","sequence":"additional","affiliation":[{"name":"Sichuan GreatWall Computer System Co., Ltd., Luzhou 646000, China"}]},{"given":"Xuewen","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Sichuan University, Chengdu 610065, China"}]},{"given":"Dasha","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Sichuan University, Chengdu 610065, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6166-890X","authenticated-orcid":false,"given":"Liangyin","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science, Sichuan University, Chengdu 610065, China"},{"name":"Institute for Industrial Internet Research, Sichuan University, Chengdu 610065, China"}]},{"given":"Xiaoqing","family":"Xing","sequence":"additional","affiliation":[{"name":"College of Aviation Engineering, Civil Aviation Flight University of China, Guanghan 618307, China"}]},{"given":"Yuming","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Sichuan University, Chengdu 610065, China"},{"name":"Institute for Industrial Internet Research, Sichuan University, Chengdu 610065, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2789","DOI":"10.2147\/DMSO.S312787","article-title":"Artificial Flora Algorithm-Based Feature Selection with Gradient Boosted Tree Model for Diabetes Classification","volume":"14","author":"Nagaraj","year":"2021","journal-title":"Diabetes Metab. Syndr. Obes."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft coco: Common objects in context. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1016\/j.ins.2021.08.030","article-title":"A fusing framework of shortcut convolutional neural networks","volume":"579","author":"Zhang","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1016\/j.ins.2021.09.041","article-title":"A neural network architecture optimizer based on DARTS and generative adversarial learning","volume":"581","author":"Zhang","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"108459","DOI":"10.1016\/j.knosys.2022.108459","article-title":"Adaptive feature fusion for time series classification","volume":"243","author":"Wang","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1109\/LSP.2021.3056901","article-title":"A Multi-Task CNN for Maritime Target Detection","volume":"28","author":"Liu","year":"2021","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, Q., Arnab, A., and Torr, P.H. (2018, January 8\u201314). Weakly-and semi-supervised panoptic segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01267-0_7"},{"key":"ref_8","unstructured":"Bellver Bueno, M., Salvador Aguilera, A., Torres Vi\u00f1als, J., and Gir\u00f3 Nieto, X. (2019, January 16\u201320). Budget-aware semi-supervised semantic and instance segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Long Beach, CA, USA."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hu, R., Doll\u00e1r, P., He, K., Darrell, T., and Girshick, R. (2018, January 18\u201323). Learning to segment every thing. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00445"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wei, Y., Xiao, H., Shi, H., Jie, Z., Feng, J., and Huang, T.S. (2018, January 18\u201323). Revisiting dilated convolution: A simple approach for weakly-and semi-supervised semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00759"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, Y., Yuan, L., and Vasconcelos, N. (2019, January 15\u201320). Bidirectional learning for domain adaptation of semantic segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00710"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zamir, A.R., Sax, A., Shen, W., Guibas, L.J., Malik, J., and Savarese, S. (2018, January 18\u201323). Taskonomy: Disentangling task transfer learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00391"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., and Liu, C. (2018). A survey on deep transfer learning. Artificial Neural Networks and Machine Learning\u2014ICANN 2018, Proceedings of the International Conference on Artificial Neural Networks, Rhodes, Greece, 4\u20137 October 2018, Springer.","DOI":"10.1007\/978-3-030-01424-7_27"},{"key":"ref_15","unstructured":"Bahdanau, D., Cho, K., and Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv."},{"key":"ref_16","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 3\u20139). Attention is all you need. Proceedings of the Advances in Neural Information Processing Systems 30, Long Beach, CA, USA."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hong, S., Oh, J., Lee, H., and Han, B. (2016, January 27\u201330). Learning transferrable knowledge for semantic segmentation with deep convolutional neural network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.349"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S., and Zhang, L. (2018, January 18\u201322). Bottom-up and top-down attention for image captioning and visual question answering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00636"},{"key":"ref_19","unstructured":"Mnih, V., Heess, N., Graves, A., and Kavukcuoglu, K. (2014, January 8\u201313). Recurrent models of visual attention. Proceedings of the Advances in Neural Information Processing Systems 27, Montreal, QC, Canada."},{"key":"ref_20","unstructured":"Wang, F., and Tax, D.M. (2016). Survey on the attention based RNN model and its applications in computer vision. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Guo, H., Zheng, K., Fan, X., Yu, H., and Wang, S. (2019, January 15\u201320). Visual attention consistency under image transforms for multi-label image classification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00082"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Papandreou, G., Chen, L.C., Murphy, K.P., and Yuille, A.L. (2015, January 7\u201313). Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.203"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lee, J., Kim, E., Lee, S., Lee, J., and Yoon, S. (2019, January 15\u201320). Ficklenet: Weakly and semi-supervised semantic image segmentation using stochastic inference. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00541"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A. (2016, January 27\u201330). Learning deep features for discriminative localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"101897","DOI":"10.1016\/j.artmed.2020.101897","article-title":"A shape context fully convolutional neural network for segmentation and classification of cervical nuclei in Pap smear images","volume":"107","author":"Hussain","year":"2020","journal-title":"Artif. Intell. Med."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/22\/8794\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:17:55Z","timestamp":1760145475000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/22\/8794"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,14]]},"references-count":25,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["s22228794"],"URL":"https:\/\/doi.org\/10.3390\/s22228794","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,11,14]]}}}