Results 21 to 30 of about 59,151 (296)
Frequency Characteristics Guided Network for Few-Shot SAR Target Recognition
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingContemporary research in synthetic aperture radar (SAR) automatic target recognition (ATR) reveals that few-shot learning algorithms can attain exceptional classification accuracy through training paradigms employing several hundred to thousands of ...
Fei Gao +5 more
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Cross-lingual few-shot sign language recognition
Pattern RecognitionThere are over 150 sign languages worldwide, each with numerous local variants and thousands of signs. However, collecting annotated data for each sign language to train a model is a laborious and expert-dependent task. To address this issue, this paper introduces the problem of few-shot sign language recognition (FSSLR) in a cross-lingual setting. The
Yunus Can Bilge +2 more
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Advancing Few-Shot Named Entity Recognition with Large Language Model
Applied SciencesFew-shot named entity recognition (NER) involves identifying specific entities using limited data. Metric learning-based methods, which compute token-level similarities between query and support sets to identify target entities, have demonstrated ...
Yuhui Xiao, Jianjian Zou, Qun Yang
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Few-shot Open-set Recognition by Transformation Consistency [PDF]
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 In this paper, we attack a few-shot open-set recognition (FSOSR) problem, which is a combination of few-shot learning (FSL) and open-set recognition (OSR). It aims to quickly adapt a model to a given small set of labeled samples while rejecting unseen class samples.
Minki Jeong, Seokeon Choi, Changick Kim
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Multimodal Few-Shot Learning for Gait Recognition
Applied Sciences, 2020 A person’s gait is a behavioral trait that is uniquely associated with each individual and can be used to recognize the person. As information about the human gait can be captured by wearable devices, a few studies have led to the proposal of methods to ...
Jucheol Moon +3 more
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Few-shot Named Entity Ｒecognition for Medical Text
Journal of Harbin University of Science and Technology, 2021 Aiming at the problem that medical text named entity recognition lacks sufficient labeled data，a newly named entity recognition deep neural network and data enhancement method is proposed.
QIN Jian +3 more
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Semantic Prompt for Few-Shot Image Recognition
CoRR, 2023 Few-shot learning is a challenging problem since only a few examples are provided to recognize a new class. Several recent studies exploit additional semantic information, e.g. text embeddings of class names, to address the issue of rare samples through combining semantic prototypes with visual prototypes.
Wentao Chen +5 more
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Learning Compositional Representations for Few-Shot Recognition [PDF]
2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019 One of the key limitations of modern deep learning approaches lies in the amount of data required to train them. Humans, by contrast, can learn to recognize novel categories from just a few examples. Instrumental to this rapid learning ability is the compositional structure of concept representations in the human brain --- something that deep learning ...
Pavel Tokmakov +2 more
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Learning multi-level weight-centric features for few-shot learning
, 2022 Few-shot learning is currently enjoying a considerable resurgence of interest, aided by the recent advance of deep learning. Contemporary approaches based on weight-generation scheme delivers a straightforward and flexible solution to the problem ...
Liang, Mingjiang +4 more
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Cloze-Style Data Augmentation for Few-Shot Intent Recognition
Mathematics, 2022 Intent recognition aims to identify users’ potential intents from their utterances, which is a key component in task-oriented dialog systems. A real challenge, however, is that the number of intent categories has grown faster than human-annotated data ...
Xin Zhang +4 more
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