Learning Saliency From Single Noisy Labelling : A Robust Model Fitting Perspective
The advances made in predicting visual saliency using deep neural networks come at the expense of collecting large-scale annotated data. However, pixel-wise annotation is labor-intensive and overwhelming. In this paper, we propose to learn saliency prediction from a single noisy labelling, which is...
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Détails bibliographiques
| Publié dans: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 43(2021), 8 vom: 22. Aug., Seite 2866-2873
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| Auteur principal: |
Zhang, Jing
(Auteur) |
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| Autres auteurs: |
Dai, Yuchao,
Zhang, Tong,
Harandi, Mehrtash,
Barnes, Nick,
Hartley, Richard |
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| Format: | Article en ligne
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| Langue: | English |
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| Publié: |
2021
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| Accès à la collection: | IEEE transactions on pattern analysis and machine intelligence
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| Sujets: | Journal Article
Research Support, Non-U.S. Gov't |
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