Not All Samples are Trustworthy Towards Deep Robust SVP Prediction

Not All Samples are Trustworthy : Towards Deep Robust SVP Prediction

In this paper, we study the problem of estimating subjective visual properties (SVP) for images, which is an emerging task in Computer Vision. Generally speaking, collecting SVP datasets involves a crowdsourcing process where annotations are obtained from a wide range of online users. Since the proc...

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Détails bibliographiques
Publié dans:IEEE transactions on pattern analysis and machine intelligence. - 1979. - 44(2022), 6 vom: 29. Juni, Seite 3154-3169
Auteur principal: Xu, Qianqian (Auteur)
Autres auteurs: Yang, Zhiyong, Jiang, Yangbangyan, Cao, Xiaochun, Yao, Yuan, Huang, Qingming
Format: Article en ligne
Langue:English
Publié: 2022
Accès à la collection:IEEE transactions on pattern analysis and machine intelligence
Sujets:Journal Article Research Support, Non-U.S. Gov't
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520 |a In this paper, we study the problem of estimating subjective visual properties (SVP) for images, which is an emerging task in Computer Vision. Generally speaking, collecting SVP datasets involves a crowdsourcing process where annotations are obtained from a wide range of online users. Since the process is done without quality control, SVP datasets are known to suffer from noise. This leads to the issue that not all samples are trustworthy. Facing this problem, we need to develop robust models for learning SVP from noisy crowdsourced annotations. In this paper, we construct two general robust learning frameworks for this application. Specifically, in the first framework, we propose a probabilistic framework to explicitly model the sparse unreliable patterns that exist in the dataset. It is noteworthy that we then provide an alternative framework that could reformulate the sparse unreliable patterns as a "contraction" operation over the original loss function. The latter framework leverages not only efficient end-to-end training but also rigorous theoretical analyses. To apply these frameworks, we further provide two models as implementations of the frameworks, where the sparse noise parameters could be interpreted with the HodgeRank theory. Finally, extensive theoretical and empirical studies show the effectiveness of our proposed framework 
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700 1 |a Yang, Zhiyong  |e verfasserin  |4 aut 
700 1 |a Jiang, Yangbangyan  |e verfasserin  |4 aut 
700 1 |a Cao, Xiaochun  |e verfasserin  |4 aut 
700 1 |a Yao, Yuan  |e verfasserin  |4 aut 
700 1 |a Huang, Qingming  |e verfasserin  |4 aut 
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