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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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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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Tingkai Hu, Zuqin Chen, Jike Ge, Zhaoxu Yang and Jichao Xu
Insufficiently labeled samples and low-generalization performance have become significant natural language processing problems, drawing significant concern for few-shot text classification (FSTC). Advances in prompt learning have significantly improved t...
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Junpeng Wu and Yibo Zhou
To address the issue of low accuracy in insulator object detection within power systems due to a scarcity of image sample data, this paper proposes a method for identifying insulator objects based on improved few-shot object detection through feature rew...
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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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Yaohui Hu, Chun Liu, Zheng Li, Junkui Xu, Zhigang Han and Jianzhong Guo
Buildings are important entity objects of cities, and the classification of building shapes plays an indispensable role in the cognition and planning of the urban structure. In recent years, some deep learning methods have been proposed for recognizing t...
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Yao Zhao, Guangxia Wang, Jian Yang, Lantian Zhang and Xiaofei Qi
The geographical feature extraction of historical maps is an important foundation for realizing the transition from human map reading to machine map reading. The current methods for building block extraction from historical maps have many problems, such ...
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Gabriel Dahia and Maurício Pamplona Segundo
We propose a method that can perform one-class classification given only a small number of examples from the target class and none from the others. We formulate the learning of meaningful features for one-class classification as a meta-learning problem i...
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Xuyang Wang, Yajun Du, Danroujing Chen, Xianyong Li, Xiaoliang Chen, Yongquan Fan, Chunzhi Xie, Yanli Li and Jia Liu
Domain-generalized few-shot text classification (DG-FSTC) is a new setting for few-shot text classification (FSTC). In DG-FSTC, the model is meta-trained on a multi-domain dataset, and meta-tested on unseen datasets with different domains. However, previ...
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Wenfeng Zheng, Xia Tian, Bo Yang, Shan Liu, Yueming Ding, Jiawei Tian and Lirong Yin
Learning information from a single or a few samples is called few-shot learning. This learning method will solve deep learning?s dependence on a large sample. Deep learning achieves few-shot learning through meta-learning: ?how to learn by using previous...
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Tianshu Zhang, Wenwen Dai, Zhiyu Chen, Sai Yang, Fan Liu and Hao Zheng
Due to their compelling performance and appealing simplicity, metric-based meta-learning approaches are gaining increasing attention for addressing the challenges of few-shot image classification. However, many similar methods employ intricate network ar...
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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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Dan Liu, Ting Liu, Hai Bi, Yunpeng Zhao and Yuan Cheng
In the marine ecological environment, marine microalgae is an important photosynthetic autotrophic organism, which can carry out photosynthesis and absorb carbon dioxide. With the increasingly serious eutrophication of the water body, under certain envir...
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Haoming Liang, Jinze Du, Hongchen Zhang, Bing Han and Yan Ma
Recently, few-shot learning has attracted significant attention in the field of video action recognition, owing to its data-efficient learning paradigm. Despite the encouraging progress, identifying ways to further improve the few-shot learning performan...
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Yingying Liang, Peng Zhao and Yimeng Wang
Deep learning has undergone significant progress for machinery fault diagnosis in the Industrial Internet of Things; however, it requires a substantial amount of labeled data. The lack of sufficient fault samples in practical applications remains a chall...
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Yuqing Hu, Stéphane Pateux and Vincent Gripon
In many real-life problems, it is difficult to acquire or label large amounts of data, resulting in so-called few-shot learning problems. However, few-shot classification is a challenging problem due to the uncertainty caused by using few labeled samples...
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Jingyao Li, Lianglun Cheng, Zewen Zheng, Jiahong Chen, Genping Zhao and Zeng Lu
The datasets in the latest semantic segmentation model often need to be manually labeled for each pixel, which is time-consuming and requires much effort. General models are unable to make better predictions, for new categories of information that have n...
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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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Jinting Zhu, Julian Jang-Jaccard, Amardeep Singh, Paul A. Watters and Seyit Camtepe
Malware authors apply different techniques of control flow obfuscation, in order to create new malware variants to avoid detection. Existing Siamese neural network (SNN)-based malware detection methods fail to correctly classify different malware familie...
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Zhipeng Zhang, Shengquan Liu and Jianming Cheng
In recent years, large-scale pretrained language models have become widely used in natural language processing tasks. On this basis, prompt learning has achieved excellent performance in specific few-shot classification scenarios. The core idea of prompt...
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