Results 31 to 40 of about 210,501 (299)
CoRR, 2019 Recent literature has shown that symbolic data, such as text and graphs, is often better represented by points on a curved manifold, rather than in Euclidean space. However, geometrical operations on manifolds are generally more complicated than in Euclidean space, and thus many techniques for processing and analysis taken for granted in Euclidean ...
Max Aalto, Nakul Verma
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Query-dependent metric learning for adaptive, content-based image browsing and retrieval [PDF]
, 2014 Content-based image retrieval (CBIR) systems often incorporate a relevance feedback mechanism in which retrieval is adapted based on users identifying images as relevant or irrelevant.
Han, Junwei, McKenna, Stephen; id_orcid
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Gong-kuang zidonghua, 2023 In the traditional personnel re-recognition method in coal mine underground based on metric learning, because metric learning ignores the absolute distance between positive and negative samples, the gradient of the loss function disappears or disperses ...
ZHANG Liya, WANG Yu, HAO Bonan
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Multi-Sensors System and Deep Learning Models for Object Tracking
Sensors, 2023 Autonomous navigation relies on the crucial aspect of perceiving the environment to ensure the safe navigation of an autonomous platform, taking into consideration surrounding objects and their potential movements. Consequently, a fundamental requirement
Ghina El Natour +2 more
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A Multi-Layer Feature Fusion Method for Few-Shot Image Classification
Sensors, 2023 In image classification, few-shot learning deals with recognizing visual categories from a few tagged examples. The degree of expressiveness of the encoded features in this scenario is a crucial question that needs to be addressed in the models being ...
Jacó C. Gomes +2 more
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A review on multi-task metric learning
Big Data Analytics, 2018 Distance metric plays an important role in machine learning which is crucial to the performance of a range of algorithms. Metric learning, which refers to learning a proper distance metric for a particular task, has attracted much attention in machine ...
Peipei Yang, Kaizhu Huang, Amir Hussain
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Metric Learning for Keyword Spotting [PDF]
2021 IEEE Spoken Language Technology Workshop (SLT), 2021 The goal of this work is to train effective representations for keyword spotting via metric learning. Most existing works address keyword spotting as a closed-set classification problem, where both target and non-target keywords are predefined. Therefore, prevailing classifier-based keyword spotting systems perform poorly on non-target sounds which are
Jaesung Huh +4 more
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Distributed Semi-Supervised Metric Learning
IEEE Access, 2016 Over the last decade, many pairwise-constraint-based metric learning algorithms have been developed to automatically learn application-specific metrics from data under similarity/dissimilarity data-pair constraints (weak labels).
Pengcheng Shen, Xin Du, Chunguang Li
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Research Progress on Few-Shot Learning for Remote Sensing Image Interpretation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 The rapid development of deep learning brings effective solutions for remote sensing image interpretation. Training deep neural network models usually require a large number of manually labeled samples. However, there is a limitation to obtain sufficient
Xian Sun +5 more
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Proceedings of the 26th International Conference on World Wide Web, 2017 Metric learning algorithms produce distance metrics that capture the important relationships among data. In this work, we study the connection between metric learning and collaborative filtering. We propose Collaborative Metric Learning (CML) which learns a joint metric space to encode not only users' preferences but also the user-user and item-item ...
Cheng-Kang Hsieh +5 more
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