{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:27:47Z","timestamp":1760171267327,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":40,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,6,27]],"date-time":"2022-06-27T00:00:00Z","timestamp":1656288000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100004415","name":"North Atlantic Treaty Organization","doi-asserted-by":"publisher","award":["SPS G5711"],"award-info":[{"award-number":["SPS G5711"]}],"id":[{"id":"10.13039\/100004415","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,6,27]]},"DOI":"10.1145\/3512527.3531383","type":"proceedings-article","created":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T22:23:32Z","timestamp":1656023012000},"page":"286-294","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Automatic Visual Recognition of Unexploded Ordnances Using Supervised Deep Learning"],"prefix":"10.1145","author":[{"given":"Georgios","family":"Begkas","sequence":"first","affiliation":[{"name":"ITI - CERTH, Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Panagiotis","family":"Giannakeris","sequence":"additional","affiliation":[{"name":"ITI - CERTH, Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Konstantinos","family":"Ioannidis","sequence":"additional","affiliation":[{"name":"ITI - CERTH, Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Georgios","family":"Kalpakis","sequence":"additional","affiliation":[{"name":"ITI - CERTH, Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Theodora","family":"Tsikrika","sequence":"additional","affiliation":[{"name":"ITI - CERTH, Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefanos","family":"Vrochidis","sequence":"additional","affiliation":[{"name":"ITI - CERTH, Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ioannis","family":"Kompatsiaris","sequence":"additional","affiliation":[{"name":"ITI - CERTH, Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,6,27]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Landmine Detection and Classification Using MLP. In 2011 Third International Conference on Computational Intelligence, Modelling & Simulation. IEEE","author":"Achkar Roger","year":"2011","unstructured":"Roger Achkar , Michel Owayjan , and Carlo Mrad . 2011 . Landmine Detection and Classification Using MLP. In 2011 Third International Conference on Computational Intelligence, Modelling & Simulation. IEEE , Langkawi, Malaysia, 1--6. https:\/\/doi.org\/10.1109\/CIMSim. 2011.10 10.1109\/CIMSim.2011.10 Roger Achkar, Michel Owayjan, and Carlo Mrad. 2011. Landmine Detection and Classification Using MLP. In 2011 Third International Conference on Computational Intelligence, Modelling & Simulation. IEEE, Langkawi, Malaysia, 1--6. https:\/\/doi.org\/10.1109\/CIMSim.2011.10"},{"volume-title":"Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII, Russell S. Harmon, John H. Holloway Jr., and J","author":"Amer Saed","key":"e_1_3_2_1_2_1","unstructured":"Saed Amer , Amir Shirkhodaie , and Haroun Rababaah . 2008. UXO detection, characterization, and remediation using intelligent robotic systems . In Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII, Russell S. Harmon, John H. Holloway Jr., and J . Thomas Broach (Eds.), Vol. 6953 . International Society for Optics and Photonics, SPIE , Orlando, FL , 191 -- 202. https:\/\/doi.org\/10.1117\/12.777778 10.1117\/12.777778 Saed Amer, Amir Shirkhodaie, and Haroun Rababaah. 2008. UXO detection, characterization, and remediation using intelligent robotic systems. In Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII, Russell S. Harmon, John H. Holloway Jr., and J. Thomas Broach (Eds.), Vol. 6953. International Society for Optics and Photonics, SPIE, Orlando, FL, 191 -- 202. https:\/\/doi.org\/10.1117\/12.777778"},{"key":"e_1_3_2_1_3_1","volume-title":"Serge Belongie, and Pietro Perona.","author":"Branson Steve","year":"2014","unstructured":"Steve Branson , Grant Van Horn , Serge Belongie, and Pietro Perona. 2014 . Bird species categorization using pose normalized deep convolutional nets. arXiv preprint arXiv:1406.2952 (2014). Steve Branson, Grant Van Horn, Serge Belongie, and Pietro Perona. 2014. Bird species categorization using pose normalized deep convolutional nets. arXiv preprint arXiv:1406.2952 (2014)."},{"key":"e_1_3_2_1_4_1","volume-title":"Collective Awareness to Unexploded Ordnance. https:\/\/cat-uxo.com\/ Retrieved","author":"CAT-UXO.","year":"2021","unstructured":"CAT-UXO. 2021. Collective Awareness to Unexploded Ordnance. https:\/\/cat-uxo.com\/ Retrieved March 15, 2021 from CAT-UXO. 2021. Collective Awareness to Unexploded Ordnance. https:\/\/cat-uxo.com\/ Retrieved March 15, 2021 from"},{"key":"e_1_3_2_1_5_1","volume-title":"Le","author":"Cubuk Ekin D.","year":"2019","unstructured":"Ekin D. Cubuk , Barret Zoph , Jonathon Shlens , and Quoc V . Le . 2019 . RandAugment: Practical data augmentation with no separate search. CoRR , Vol. abs\/ 1909 .13719 (2019). http:\/\/arxiv.org\/abs\/1909.13719 Ekin D. Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V. Le. 2019. RandAugment: Practical data augmentation with no separate search. CoRR, Vol. abs\/1909.13719 (2019). http:\/\/arxiv.org\/abs\/1909.13719"},{"key":"e_1_3_2_1_6_1","volume-title":"Belongie","author":"Cui Yin","year":"2018","unstructured":"Yin Cui , Yang Song , Chen Sun , Andrew Howard , and Serge J . Belongie . 2018 . Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning. CoRR , Vol. abs\/ 1806 .06193 (2018). http:\/\/arxiv.org\/abs\/1806.06193 Yin Cui, Yang Song, Chen Sun, Andrew Howard, and Serge J. Belongie. 2018. Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning. CoRR, Vol. abs\/1806.06193 (2018). http:\/\/arxiv.org\/abs\/1806.06193"},{"key":"e_1_3_2_1_7_1","volume-title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. CoRR","author":"Dosovitskiy Alexey","year":"1929","unstructured":"Alexey Dosovitskiy , Lucas Beyer , Alexander Kolesnikov , Dirk Weissenborn , Xiaohua Zhai , Thomas Unterthiner , Mostafa Dehghani , Matthias Minderer , Georg Heigold , Sylvain Gelly , Jakob Uszkoreit , and Neil Houlsby . 2020. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. CoRR , Vol. abs\/ 2010 .1 1929 (2020). https:\/\/arxiv.org\/abs\/2010.11929 Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2020. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. CoRR, Vol. abs\/2010.11929 (2020). https:\/\/arxiv.org\/abs\/2010.11929"},{"key":"e_1_3_2_1_8_1","volume-title":"Deep learning-enabled medical computer vision. NPJ digital medicine","author":"Esteva Andre","year":"2021","unstructured":"Andre Esteva , Katherine Chou , Serena Yeung , Nikhil Naik , Ali Madani , Ali Mottaghi , Yun Liu , Eric Topol , Jeff Dean , and Richard Socher . 2021. Deep learning-enabled medical computer vision. NPJ digital medicine , Vol. 4 , 1 ( 2021 ), 1--9. Andre Esteva, Katherine Chou, Serena Yeung, Nikhil Naik, Ali Madani, Ali Mottaghi, Yun Liu, Eric Topol, Jeff Dean, and Richard Socher. 2021. Deep learning-enabled medical computer vision. NPJ digital medicine, Vol. 4, 1 (2021), 1--9."},{"key":"e_1_3_2_1_9_1","volume-title":"Yuille","author":"He Ju","year":"2021","unstructured":"Ju He , Jieneng Chen , Shuai Liu , Adam Kortylewski , Cheng Yang , Yutong Bai , Changhu Wang , and Alan L . Yuille . 2021 . TransFG: A Transformer Architecture for Fine-grained Recognition. CoRR , Vol. abs\/ 2103 .07976 (2021). https:\/\/arxiv.org\/abs\/2103.07976 Ju He, Jieneng Chen, Shuai Liu, Adam Kortylewski, Cheng Yang, Yutong Bai, Changhu Wang, and Alan L. Yuille. 2021. TransFG: A Transformer Architecture for Fine-grained Recognition. CoRR, Vol. abs\/2103.07976 (2021). https:\/\/arxiv.org\/abs\/2103.07976"},{"key":"e_1_3_2_1_10_1","volume-title":"Deep Residual Learning for Image Recognition. CoRR","author":"He Kaiming","year":"2015","unstructured":"Kaiming He , Xiangyu Zhang , Shaoqing Ren , and Jian Sun . 2015. Deep Residual Learning for Image Recognition. CoRR , Vol. abs\/ 1512 .03385 ( 2015 ). http:\/\/arxiv.org\/abs\/1512.03385 Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. Deep Residual Learning for Image Recognition. CoRR, Vol. abs\/1512.03385 (2015). http:\/\/arxiv.org\/abs\/1512.03385"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1561\/0600000079"},{"key":"e_1_3_2_1_13_1","volume-title":"Combining weakly and webly supervised learning for classifying food images. arXiv preprint arXiv:1712.08730","author":"Kaur Parneet","year":"2017","unstructured":"Parneet Kaur , Karan Sikka , and Ajay Divakaran . 2017. Combining weakly and webly supervised learning for classifying food images. arXiv preprint arXiv:1712.08730 ( 2017 ). Parneet Kaur, Karan Sikka, and Ajay Divakaran. 2017. Combining weakly and webly supervised learning for classifying food images. arXiv preprint arXiv:1712.08730 (2017)."},{"key":"e_1_3_2_1_14_1","volume-title":"Novel Dataset for Fine-Grained Image Categorization. In First Workshop on Fine-Grained Visual Categorization, IEEE Conference on Computer Vision and Pattern Recognition","author":"Khosla Aditya","year":"2011","unstructured":"Aditya Khosla , Nityananda Jayadevaprakash , Bangpeng Yao , and Li Fei-Fei . 2011 . Novel Dataset for Fine-Grained Image Categorization. In First Workshop on Fine-Grained Visual Categorization, IEEE Conference on Computer Vision and Pattern Recognition . Colorado Springs, CO. Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao, and Li Fei-Fei. 2011. Novel Dataset for Fine-Grained Image Categorization. In First Workshop on Fine-Grained Visual Categorization, IEEE Conference on Computer Vision and Pattern Recognition. Colorado Springs, CO."},{"key":"e_1_3_2_1_15_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba . 2015 . Adam : A Method for Stochastic Optimization. CoRR , Vol. abs\/ 1412 .6980 (2015). Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. CoRR, Vol. abs\/1412.6980 (2015)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01270-0_48"},{"key":"e_1_3_2_1_17_1","volume-title":"Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems","author":"Krizhevsky Alex","year":"2012","unstructured":"Alex Krizhevsky , Ilya Sutskever , and Geoffrey E Hinton . 2012. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems , Vol. 25 ( 2012 ), 1097--1105. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, Vol. 25 (2012), 1097--1105."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2017.000-1"},{"key":"e_1_3_2_1_19_1","unstructured":"Yann LeCun Yoshua Bengio etal 1995. Convolutional networks for images speech and time series. The handbook of brain theory and neural networks Vol. 3361 10 (1995) 1995.  Yann LeCun Yoshua Bengio et al. 1995. Convolutional networks for images speech and time series. The handbook of brain theory and neural networks Vol. 3361 10 (1995) 1995."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_1_21_1","volume-title":"SGDR: Stochastic Gradient Descent with Restarts. CoRR","author":"Loshchilov Ilya","year":"2016","unstructured":"Ilya Loshchilov and Frank Hutter . 2016 . SGDR: Stochastic Gradient Descent with Restarts. CoRR , Vol. abs\/ 1608 .03983 (2016). http:\/\/arxiv.org\/abs\/1608.03983 Ilya Loshchilov and Frank Hutter. 2016. SGDR: Stochastic Gradient Descent with Restarts. CoRR, Vol. abs\/1608.03983 (2016). http:\/\/arxiv.org\/abs\/1608.03983"},{"key":"e_1_3_2_1_22_1","unstructured":"Azadeh Nazemi Niloofar Tavakolian Donal Fitzpatrick Ching Y Suen etal 2019. Offline handwritten mathematical symbol recognition utilising deep learning. arXiv preprint arXiv:1910.07395 (2019).  Azadeh Nazemi Niloofar Tavakolian Donal Fitzpatrick Ching Y Suen et al. 2019. Offline handwritten mathematical symbol recognition utilising deep learning. arXiv preprint arXiv:1910.07395 (2019)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICVGIP.2008.47"},{"key":"e_1_3_2_1_24_1","volume-title":"GPR signal characterization for automated landmine and UXO detection based on machine learning techniques. Remote sensing","author":"Nieto Xavier N\u00fa","year":"2014","unstructured":"Xavier N\u00fa nez- Nieto , Mercedes Solla , Paula G\u00f3mez-P\u00e9rez , and Henrique Lorenzo . 2014. GPR signal characterization for automated landmine and UXO detection based on machine learning techniques. Remote sensing , Vol. 6 , 10 ( 2014 ), 9729--9748. Xavier N\u00fa nez-Nieto, Mercedes Solla, Paula G\u00f3mez-P\u00e9rez, and Henrique Lorenzo. 2014. GPR signal characterization for automated landmine and UXO detection based on machine learning techniques. Remote sensing, Vol. 6, 10 (2014), 9729--9748."},{"key":"e_1_3_2_1_25_1","volume-title":"NeurIPS 2021 Workshop on Distribution Shifts: Connecting Methods and Applications.","author":"Pinto Francesco","year":"2021","unstructured":"Francesco Pinto , Philip Torr , and Puneet K Dokania . 2021 . Are Vision Transformers Always More Robust Than Convolutional Neural Networks? . In NeurIPS 2021 Workshop on Distribution Shifts: Connecting Methods and Applications. Francesco Pinto, Philip Torr, and Puneet K Dokania. 2021. Are Vision Transformers Always More Robust Than Convolutional Neural Networks?. In NeurIPS 2021 Workshop on Distribution Shifts: Connecting Methods and Applications."},{"key":"e_1_3_2_1_26_1","volume-title":"On the momentum term in gradient descent learning algorithms. Neural networks","author":"Qian Ning","year":"1999","unstructured":"Ning Qian . 1999. On the momentum term in gradient descent learning algorithms. Neural networks , Vol. 12 , 1 ( 1999 ), 145--151. Ning Qian. 1999. On the momentum term in gradient descent learning algorithms. Neural networks, Vol. 12, 1 (1999), 145--151."},{"key":"e_1_3_2_1_27_1","volume-title":"Advances in Neural Information Processing Systems","volume":"34","author":"Raghu Maithra","year":"2021","unstructured":"Maithra Raghu , Thomas Unterthiner , Simon Kornblith , Chiyuan Zhang , and Alexey Dosovitskiy . 2021 . Do vision transformers see like convolutional neural networks ? Advances in Neural Information Processing Systems , Vol. 34 (2021). Maithra Raghu, Thomas Unterthiner, Simon Kornblith, Chiyuan Zhang, and Alexey Dosovitskiy. 2021. Do vision transformers see like convolutional neural networks? Advances in Neural Information Processing Systems, Vol. 34 (2021)."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"e_1_3_2_1_29_1","volume-title":"Detection and Remediation Technologies for Mines and Minelike Targets XII","author":"Shirkhodaie Amir","year":"1977","unstructured":"Amir Shirkhodaie and Haroun Rababaah . 2007. Visual detection, recognition, and classification of surface-buried UXO based on soft-computing decision fusion . In Detection and Remediation Technologies for Mines and Minelike Targets XII , Russell S. Harmon, J. Thomas Broach, and John H. Holloway Jr. (Eds.), Vol. 6553 . International Society for Optics and Photonics, SPIE , 650 -- 661. https:\/\/doi.org\/10.1117\/12.7 1977 6 10.1117\/12.719776 Amir Shirkhodaie and Haroun Rababaah. 2007. Visual detection, recognition, and classification of surface-buried UXO based on soft-computing decision fusion. In Detection and Remediation Technologies for Mines and Minelike Targets XII, Russell S. Harmon, J. Thomas Broach, and John H. Holloway Jr. (Eds.), Vol. 6553. International Society for Optics and Photonics, SPIE, 650 -- 661. https:\/\/doi.org\/10.1117\/12.719776"},{"key":"e_1_3_2_1_30_1","volume-title":"Very Deep Convolutional Networks for Large-Scale Image Recognition. CoRR","author":"Simonyan Karen","year":"2015","unstructured":"Karen Simonyan and Andrew Zisserman . 2015. Very Deep Convolutional Networks for Large-Scale Image Recognition. CoRR , Vol. abs\/ 1409 .1556 ( 2015 ). Karen Simonyan and Andrew Zisserman. 2015. Very Deep Convolutional Networks for Large-Scale Image Recognition. CoRR, Vol. abs\/1409.1556 (2015)."},{"key":"e_1_3_2_1_31_1","volume-title":"How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers. CoRR","author":"Steiner Andreas","year":"2021","unstructured":"Andreas Steiner , Alexander Kolesnikov , Xiaohua Zhai , Ross Wightman , Jakob Uszkoreit , and Lucas Beyer . 2021. How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers. CoRR , Vol. abs\/ 2106 .10270 ( 2021 ). https:\/\/arxiv.org\/abs\/2106.10270 Andreas Steiner, Alexander Kolesnikov, Xiaohua Zhai, Ross Wightman, Jakob Uszkoreit, and Lucas Beyer. 2021. How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers. CoRR, Vol. abs\/2106.10270 (2021). https:\/\/arxiv.org\/abs\/2106.10270"},{"key":"e_1_3_2_1_32_1","volume-title":"Le","author":"Tan Mingxing","year":"2018","unstructured":"Mingxing Tan , Bo Chen , Ruoming Pang , Vijay Vasudevan , and Quoc V . Le . 2018 . MnasNet: Platform- Aware Neural Architecture Search for Mobile. CoRR , Vol. abs\/ 1807 .11626 (2018). http:\/\/arxiv.org\/abs\/1807.11626 Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, and Quoc V. Le. 2018. MnasNet: Platform-Aware Neural Architecture Search for Mobile. CoRR, Vol. abs\/1807.11626 (2018). http:\/\/arxiv.org\/abs\/1807.11626"},{"key":"e_1_3_2_1_33_1","volume-title":"Le","author":"Tan Mingxing","year":"2019","unstructured":"Mingxing Tan and Quoc V . Le . 2019 . EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. CoRR , Vol. Abs\/ 1905 .11946 (2019). http:\/\/arxiv.org\/abs\/1905.11946 Mingxing Tan and Quoc V. Le. 2019. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. CoRR, Vol. Abs\/1905.11946 (2019). http:\/\/arxiv.org\/abs\/1905.11946"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inpa.2019.09.006"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298658"},{"key":"e_1_3_2_1_36_1","unstructured":"\"VECTOR\" project. 2020--2022. Virtual Evidence Capture Tool for Ordnance Recovery. https:\/\/projectvector.net\/.  \"VECTOR\" project. 2020--2022. Virtual Evidence Capture Tool for Ordnance Recovery. https:\/\/projectvector.net\/."},{"key":"e_1_3_2_1_37_1","volume-title":"Technical Report CNS-TR-2011-001. California Institute of Technology.","author":"Wah C.","year":"2011","unstructured":"C. Wah , S. Branson , P. Welinder , P. Perona , and S. Belongie . 2011 . The Caltech-UCSD Birds-200--2011 Dataset . Technical Report CNS-TR-2011-001. California Institute of Technology. C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie. 2011. The Caltech-UCSD Birds-200--2011 Dataset. Technical Report CNS-TR-2011-001. California Institute of Technology."},{"key":"e_1_3_2_1_38_1","first-page":"665","article-title":"Rehabilitation of landmine victims: the ultimate challenge","volume":"81","author":"Walsh Nicolas E","year":"2003","unstructured":"Nicolas E Walsh and Wendy S Walsh . 2003 . Rehabilitation of landmine victims: the ultimate challenge . Bulletin of the World Health Organization , Vol. 81 (2003), 665 -- 670 . Nicolas E Walsh and Wendy S Walsh. 2003. Rehabilitation of landmine victims: the ultimate challenge. Bulletin of the World Health Organization, Vol. 81 (2003), 665--670.","journal-title":"Bulletin of the World Health Organization"},{"key":"e_1_3_2_1_39_1","volume-title":"5th Underwater Acoustics Conference and Exhibition. Hersonissos","volume":"30","author":"Williams David P","year":"2019","unstructured":"David P Williams . 2019 . Acoustic-Color-Based Convolutional Neural Networks for UXO Classification with Low-Frequency Sonar. In John S. Papadakis (Hg.): UACE2019-Conference Proceedings . 5th Underwater Acoustics Conference and Exhibition. Hersonissos , Vol. 30 . Crete, Greece, 421--428. David P Williams. 2019. Acoustic-Color-Based Convolutional Neural Networks for UXO Classification with Low-Frequency Sonar. In John S. Papadakis (Hg.): UACE2019-Conference Proceedings. 5th Underwater Acoustics Conference and Exhibition. Hersonissos, Vol. 30. Crete, Greece, 421--428."},{"key":"e_1_3_2_1_40_1","volume-title":"Sabuncu","author":"Zhang Zhilu","year":"2018","unstructured":"Zhilu Zhang and Mert R . Sabuncu . 2018 . Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels. CoRR , Vol. abs\/ 1805 .07836 (2018). http:\/\/arxiv.org\/abs\/1805.07836 Zhilu Zhang and Mert R. Sabuncu. 2018. Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels. CoRR, Vol. abs\/1805.07836 (2018). http:\/\/arxiv.org\/abs\/1805.07836"}],"event":{"name":"ICMR '22: International Conference on Multimedia Retrieval","sponsor":["SIGMM ACM Special Interest Group on Multimedia"],"location":"Newark NJ USA","acronym":"ICMR '22"},"container-title":["Proceedings of the 2022 International Conference on Multimedia Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3512527.3531383","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3512527.3531383","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:30:12Z","timestamp":1750188612000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3512527.3531383"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,27]]},"references-count":40,"alternative-id":["10.1145\/3512527.3531383","10.1145\/3512527"],"URL":"https:\/\/doi.org\/10.1145\/3512527.3531383","relation":{},"subject":[],"published":{"date-parts":[[2022,6,27]]},"assertion":[{"value":"2022-06-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}