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Optimization of Robust Loss Functions for Weakly-Labeled Image Taxonomies: An ImageNet Case Study

McAuley, Julian; Ramisa, Amau; Caetano, Tiberio

Description

The recently proposed ImageNet dataset consists of several million images, each annotated with a single object category. However, these annotations may be imperfect, in the sense that many images contain multiple objects belonging to the label vocabulary. In other words, we have a multi-label problem but the annotations include only a single label (and not necessarily the most prominent). Such a setting motivates the use of a robust evaluation measure, which allows for a limited number of...[Show more]

CollectionsANU Research Publications
Date published: 2011
Type: Conference paper
URI: http://hdl.handle.net/1885/36037
Source: Optimization of Robust Loss Functions for Weakly-Labeled Image Taxonomies: An ImageNet Case Study
DOI: 10.1007/978-3-642-23094-3_26

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