{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:41:44Z","timestamp":1760150504179,"version":"build-2065373602"},"reference-count":28,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T00:00:00Z","timestamp":1700006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences","award":["2020YFF0400402"],"award-info":[{"award-number":["2020YFF0400402"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>One-shot object detection has been a highly demanded yet challenging task since the early age of convolutional neural networks (CNNs). For some newly started projects, a handy network that can learn the target\u2019s pattern using a single picture and automatically decide its architecture is needed. To specifically address a scenario in which a single or multiple targets are standing in relatively stable circumstances with hardly any training data, where the rough location of the target is required, we propose a one-shot simple target detection model that focuses on two main tasks: (1) deciding if the target is in the testing image, and (2) if yes, outputting the target\u2019s location in the image. This model requires no pre-training and decides its architecture automatically; therefore, it could be applied to a newly started target detection project with unconventionally simple targets and few training examples. We also propose an architecture with a non-training parameter-gaining strategy and correlation coefficient-based feedforward and activation functions, as well as easy interpretability, which might provide a perspective on studies in neural networks. We tested this design on the data we collected in our project, the Brown\u2013Yosemite dataset and part of the Mnist dataset. It successfully returned the target area in our project and obtained an IOU of up to 87.04%, reached 80.28% accuracy on the Brown\u2013Yosemite dataset with disposable networks, and obtained an accuracy of up to 89.4% on part of the Mnist dataset in the detection task.<\/jats:p>","DOI":"10.3390\/s23229188","type":"journal-article","created":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T00:47:48Z","timestamp":1700009268000},"page":"9188","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["One-Shot Simple Pattern Detection without Pre-Training and Gradient-Based Strategy"],"prefix":"10.3390","volume":"23","author":[{"given":"Jun","family":"Su","sequence":"first","affiliation":[{"name":"Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200050, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"He","sequence":"additional","affiliation":[{"name":"Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200050, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingguan","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200050, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Runze","family":"Ma","sequence":"additional","affiliation":[{"name":"Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200050, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,15]]},"reference":[{"key":"ref_1","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster r-cnn: Towards real-time object detection with region proposal networks. Proceedings of the 28th International Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE international conference on computer vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_5","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_7","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You only look once: Unified, real-time object detection. Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA."},{"key":"ref_8","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_9","unstructured":"(2023, August 12). Glenn Jocher; Ayush Chaurasia; Jing Qiu; YOLO by Ultralytics (version: 8.0.0). Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_10","unstructured":"Li, W., Wang, Z., Yang, X., Dong, C., Tian, P., Qin, T., Huo, J., Shi, Y., Wang, L., and Gao, Y. (2021). LibFewShot: A Comprehensive Library for Few-shot Learning. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4483","DOI":"10.1007\/s10462-021-10004-4","article-title":"A survey of deep meta-learning","volume":"54","author":"Huisman","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_12","first-page":"63","article-title":"Generalizing from a Few Examples: A Survey on Few-shot Learning","volume":"53","author":"Wang","year":"2020","journal-title":"ACM Comput. Surv."},{"key":"ref_13","unstructured":"Chen, W.-Y., Liu, Y.-C., Kira, Z., Wang, Y.-C.F., and Huang, J.-B. (2019). A Closer Look at Few-shot Classification. arXiv."},{"key":"ref_14","unstructured":"Finn, C., Abbeel, P., and Levine, S. (2017, January 6\u201311). Model-agnostic meta-learning for fast adaptation of deep networks. Proceedings of the 34th International Conference on Machine Learning\u2014Volume 70, Sydney, Australia."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Nataraj, L., Karthikeyan, S., Jacob, G., and Manjunath, B.S. (2011, January 20). Malware images: Visualization and automatic classification. Proceedings of the 8th International Symposium on Visualization for Cyber Security, Pittsburgh, PA, USA.","DOI":"10.1145\/2016904.2016908"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1016\/j.promfg.2020.05.146","article-title":"One-Shot Recognition of Manufacturing Defects in Steel Surfaces","volume":"48","author":"Deshpande","year":"2020","journal-title":"Procedia Manuf."},{"key":"ref_17","unstructured":"Koch, G., Zemel, R., and Salakhutdinov, R. (2015, January 6\u201311). Siamese Neural Networks for One-shot Image Recognition. Proceedings of the 32nd International Conference on Machine Learning, Lille, France."},{"key":"ref_18","unstructured":"Snell, J., Swersky, K., and Zemel, R. (2017, January 4\u20139). Prototypical networks for few-shot learning. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/BF02478259","article-title":"A logical calculus of the ideas immanent in nervous activity","volume":"5","author":"McCulloch","year":"1943","journal-title":"Bull. Math. Biophys."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1109\/TASL.2008.919072","article-title":"On the Importance of the Pearson Correlation Coefficient in Noise Reduction","volume":"16","author":"Benesty","year":"2008","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"ref_21","unstructured":"Gaier, A., and Ha, D. (2019, January 8\u201314). Weight agnostic neural networks. Proceedings of the 33rd International Conference on Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1177\/1073858413514136","article-title":"Bottom-Up and Top-Down Attention:Different Processes and Overlapping Neural Systems","volume":"20","author":"Katsuki","year":"2014","journal-title":"Neuroscientist"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kanezaki, A. (2018, January 15\u201320). Unsupervised Image Segmentation by Backpropagation. Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada.","DOI":"10.1109\/ICASSP.2018.8462533"},{"key":"ref_24","unstructured":"Harb, R., and Kn\u00f6belreiter, P. (October, January 28). Infoseg: Unsupervised semantic image segmentation with mutual information maximization. Proceedings of the DAGM German Conference on Pattern Recognition, Bonn, Germany."},{"key":"ref_25","unstructured":"Michaelis, C., Ustyuzhaninov, I., Bethge, M., and Ecker, A.S. (2018). One-shot instance segmentation. arXiv."},{"key":"ref_26","unstructured":"Zeiler, M.D., and Fergus, R. (2014). European Conference on Computer Vision, Springer."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1162\/106365602320169811","article-title":"Evolving Neural Networks through Augmenting Topologies","volume":"10","author":"Stanley","year":"2002","journal-title":"Evol. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/s41651-022-00104-2","article-title":"Constraint-based evaluation of map images generalized by deep learning","volume":"6","author":"Courtial","year":"2022","journal-title":"J. Geovisualization Spat. Anal."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/22\/9188\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:23:11Z","timestamp":1760131391000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/22\/9188"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,15]]},"references-count":28,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["s23229188"],"URL":"https:\/\/doi.org\/10.3390\/s23229188","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,11,15]]}}}