Results 21 to 30 of about 12,717 (259)
Generalized Zero-Shot Learning for Image Classification—Comparing Performance of Popular Approaches
Information, 2022 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.
Elie Saad +7 more
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Zero-shot image classification using coupled dictionary embedding
Machine Learning with Applications, 2022 Zero-shot learning (ZSL) is a framework to classify images that belong to unseen visual classes using their semantic descriptions about the unseen classes. We develop a new ZSL algorithm based on coupled dictionary learning.
Mohammad Rostami +5 more
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Probabilistic Zero-shot Classification with Semantic Rankings
CoRR, 2015 In this paper we propose a non-metric ranking-based representation of semantic similarity that allows natural aggregation of semantic information from multiple heterogeneous sources. We apply the ranking-based representation to zero-shot learning problems, and present deterministic and probabilistic zero-shot classifiers which can be built from pre ...
Jihun Hamm, Mikhail Belkin
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Zero-Shot Taxonomy Mapping for Document Classification
Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, 2023 Classification of documents according to a custom internal hierarchical taxonomy is a common problem for many organizations that deal with textual data. Approaches aimed to address this challenge are, for the vast majority, supervised methods, which have the advantage of producing good results on specific datasets, but the major drawbacks of requiring ...
Lorenzo Bongiovanni +3 more
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Zero-shot Relation Classification from Side Information [PDF]
Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021 10 pages, 8 figures, published in CIKM ...
Jiaying Gong, Hoda Eldardiry
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Zero-Shot Visual Classification with Guided Cropping
CoRR, 2023 Pretrained vision-language models, such as CLIP, show promising zero-shot performance across a wide variety of datasets. For closed-set classification tasks, however, there is an inherent limitation: CLIP image encoders are typically designed to extract generic image-level features that summarize superfluous or confounding information for the target ...
Piyapat Saranrittichai +3 more
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A Joint Label Space for Generalized Zero-Shot Classification [PDF]
IEEE Transactions on Image Processing, 2020 The fundamental problem of Zero-Shot Learning (ZSL) is that the one-hot label space is discrete, which leads to a complete loss of the relationships between seen and unseen classes. Conventional approaches rely on using semantic auxiliary information, e.g. attributes, to re-encode each class so as to preserve the inter-class associations.
Jin Li 0011 +6 more
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Zero-shot learning for requirements classification: An exploratory study
Information and Software Technology, 2023 60 pages, 22 tables, 1 ...
Alhoshan W, Ferrari A, Zhao L
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A Distance-Constrained Semantic Autoencoder for Zero-Shot Remote Sensing Scene Classification
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Zero-shot remote sensing scene classification refers to the classification of new images from unseen scene classes and has become a topic of growing interest in the field of remote sensing.
Chen Wang, Guohua Peng, Bernard De Baets
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A Survey of Zero-Shot Image Classification: Concepts, Developments, and Challenges
Tsinghua Science and TechnologyZero-shot learning is gaining increasing attention in the social computing community, primarily because it can enable models to effectively perform classification or regression tasks when new concepts continually emerge while lacking sufficient training ...
Shuai Xu +5 more
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