Object Classication from a Single Example Utilizing Class Relevance Pseudo-Metrics

By Michael Fink. 2004

28 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Zero-Shot Learning Through Cross-Modal Transfer (2013, background): “This is usually achieved by either sharing of feature representations [2], model parameters [12] or via similar context [14].”
Domain Adaptation for Visual Applications: A Comprehensive Survey (2017, background): “Existing solutions are the knowledge transfer through the reuse of model parameters [235], methods sharing parts or features [236] or”
Transfer Learning in a Transductive Setting (2013, background): “In computer vision or machine learning literature this setting is normally referred to as zero-shot learning [11, 19, 24, 9, 16] if there are no instances for the test classes available and one- or few-shot learning [16, 9, 8] if there are one or few instances available for the novel classes.”
Similarity Scores Based on Background Samples (2009, background): “The OSS draws its motivation from the growing number of so called “One-Shot Learning” techniques; that is, methods which learn from one or few training examples (see for example [12, 13]).”
What helps where – and why? Semantic relatedness for knowledge transfer (2010, background): “Similar in spirit, several works transfer knowledge in the form of learned distance metrics [3, 14, 28] or object class priors [11, 27].”
Evaluating knowledge transfer and zero-shot learning in a large-scale setting (2011, background): “The third direction is based on direct similarities to related classes effectively using the classifiers of most similar classes [1, 10, 22].”
Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics (2011, unknown): “OSS and TSS both draw their motivation from the growing number of so-called " One- Shot Learning " techniques, that is, methods which learn from one or few training examples (see for example [33], [34]).”