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Elie Saad, Marcin Paprzycki, Maria Ganzha, Amelia Badica, Costin Badica, Stefka Fidanova, Ivan Lirkov and Mirjana Ivanovic
There are many areas where conventional supervised machine learning does not work well, for instance, in cases with a large, or systematically increasing, number of countably infinite classes. Zero-shot learning has been proposed to address this. In gene...
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Sanghyun Seo and Juntae Kim
Traditional supervised learning is dependent on the label of the training data, so there is a limitation that the class label which is not included in the training data cannot be recognized properly. Therefore, zero-shot learning, which can recognize uns...
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Binghui Zhao, Liguo Han, Pan Zhang, Qiang Feng and Liyun Ma
In passive seismic exploration, the number and location of underground sources are very random, and there may be few passive sources or an uneven spatial distribution. The random distribution of seismic sources can cause the virtual shot recordings to pr...
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Chuanyun Xu, Hang Wang, Yang Zhang, Zheng Zhou and Gang Li
Few-shot learning refers to training a model with a few labeled data to effectively recognize unseen categories. Recently, numerous approaches have been suggested to improve the extraction of abundant feature information at hierarchical layers or multipl...
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Xiaodong Cui, Zhuofan He, Yangtao Xue, Keke Tang, Peican Zhu and Jing Han
Underwater Acoustic Target Recognition (UATR) plays a crucial role in underwater detection devices. However, due to the difficulty and high cost of collecting data in the underwater environment, UATR still faces the problem of small datasets. Few-shot le...
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Jaroslaw Kurek, Tomasz Latkowski, Michal Bukowski, Bartosz Swiderski, Mateusz Lepicki, Grzegorz Baranik, Bogusz Nowak, Robert Zakowicz and Lukasz Dobrakowski
In the evolving realities of recruitment, the precision of job?candidate matching is crucial. This study explores the application of Zero-Shot Recommendation AI Models to enhance this matching process. Utilizing advanced pretrained models such as all-Min...
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Zhuofan Xu, Jing Yan, Guoqing Sui, Yanze Wu, Meirong Qi, Zilong Zhang, Yingsan Geng and Jianhua Wang
High-voltage circuit breakers (HVCBs) handle the important tasks of controlling and safeguarding electricity networks. In the case of insufficient data samples, improving the accuracy of the traditional HVCB mechanical fault diagnosis method is difficult...
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Jie Ren, Changmiao Li, Yaohui An, Weichuan Zhang and Changming Sun
Few-shot fine-grained image classification (FSFGIC) methods refer to the classification of images (e.g., birds, flowers, and airplanes) belonging to different subclasses of the same species by a small number of labeled samples. Through feature representa...
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Haoyu Chen, Stacy Lindshield, Papa Ibnou Ndiaye, Yaya Hamady Ndiaye, Jill D. Pruetz and Amy R. Reibman
Few-shot learning (FSL) describes the challenge of learning a new task using a minimum amount of labeled data, and we have observed significant progress made in this area. In this paper, we explore the effectiveness of the FSL theory by considering a rea...
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Feiyang Ye, Liang Huang, Senjie Liang and KaiKai Chi
Named entity recognition (NER) in a few-shot setting is an extremely challenging task, and most existing methods fail to account for the gap between NER tasks and pre-trained language models. Although prompt learning has been successfully applied in few-...
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Chavarat Jarungvittayakon, Anak Khantachawana and Paphon Sa-ngasoongsong
Cobalt?chromium (CoCr) alloy is widely used for medical implants such as for dental or joint replacements because of its strength and high corrosion resistance. By throwing a spherical media against a material surface, fine-particle shot peening can modi...
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Peng-Yeng Yin
Air pollution has been a global issue that solicits proposals for sustainable development of social economics. Though the sources emitting pollutants are thoroughly investigated, the transportation, dispersion, scattering, and diminishing of pollutants i...
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Ayiguli Halike, Aishan Wumaier and Tuergen Yibulayin
Although low-resource relation extraction is vital in knowledge construction and characterization, more research is needed on the generalization of unknown relation types. To fill the gap in the study of low-resource (Uyghur) relation extraction methods,...
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Jian Ni, Rui Wang and Jing Tang
The detection of small objects is easily affected by background information, and a lack of context information makes detection difficult. Therefore, small object detection has become an extremely challenging task. Based on the above problems, we proposed...
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Beini Zhang, Liping Li, Yetao Lyu, Shuguang Chen, Lin Xu and Guanhua Chen
As an important part of the industrialization process, fully automated instrument monitoring and identification are experiencing an increasingly wide range of applications in industrial production, autonomous driving, and medical experimentation. However...
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Huiyuan Wang, Xiaojun Wu, Zirui Wang, Yukun Hao, Chengpeng Hao, Xinyi He and Qiao Hu
Dolphin signals are effective carriers for underwater covert detection and communication. However, the environmental and cost constraints terribly limit the amount of data available in dolphin signal datasets are often limited. Meanwhile, due to the low ...
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Guo Li, Yida Teng and Huimin Zhou
Aero engine compressor disks are typically life-limited parts and it is necessary to secure them through the implementation of an engineering plan and a manufacturing plan. Specifically, the engineering plan recommends the quantification of the safety of...
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Kazem Reza Kashyzadeh and Mahmoud Chizari
The first goal of the current study is to estimate the fatigue life of the middle steel piles of an integrated bridge installed in water and subject to the impact of sea waves. In the following, the authors have tried to improve the service life of this ...
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Shiwei Ruan, Hao Cang, Huixin Chen, Tianying Yan, Fei Tan, Yuan Zhang, Long Duan, Peng Xing, Li Guo, Pan Gao and Wei Xu
Early detection and diagnosis of crop anomalies is crucial for enhancing crop yield and quality. Recently, the combination of machine learning and deep learning with hyperspectral images has significantly improved the efficiency of crop detection. Howeve...
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Gergely Márk Csányi, Renátó Vági, Andrea Megyeri, Anna Fülöp , Dániel Nagy, János Pál Vadász and István Üveges
Few-shot learning is a deep learning subfield that is the focus of research nowadays. This paper addresses the research question of whether a triplet-trained Siamese network, initially designed for multi-class classification, can effectively handle multi...
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