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- Publisher Website: 10.1109/CVPR.2015.7298894
- Scopus: eid_2-s2.0-84959250997
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Conference Paper: Visual recognition by learning from web data: A weakly supervised domain generalization approach
Title | Visual recognition by learning from web data: A weakly supervised domain generalization approach |
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Authors | |
Issue Date | 2015 |
Citation | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015, v. 07-12-June-2015, p. 2774-2783 How to Cite? |
Abstract | In this work, we formulate a new weakly supervised domain generalization approach for visual recognition by using loosely labeled web images/videos as training data. Specifically, we aim to address two challenging issues when learning robust classifiers: 1) coping with noise in the labels of training web images/videos in the source domain; and 2) enhancing generalization capability of learnt classifiers to any unseen target domain. To address the first issue, we partition the training samples in each class into multiple clusters. By treating each cluster as a 'bag' and the samples in each cluster as 'instances', we formulate a multi-instance learning (MIL) problem by selecting a subset of training samples from each training bag and simultaneously learning the optimal classifiers based on the selected samples. To address the second issue, we assume the training web images/videos may come from multiple hidden domains with different data distributions. We then extend our MIL formulation to learn one classifier for each class and each latent domain such that multiple classifiers from each class can be effectively integrated to achieve better generalization capability. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our new approach for visual recognition by learning from web data. |
Persistent Identifier | http://hdl.handle.net/10722/321661 |
ISSN | 2023 SCImago Journal Rankings: 10.331 |
DC Field | Value | Language |
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dc.contributor.author | Niu, Li | - |
dc.contributor.author | Li, Wen | - |
dc.contributor.author | Xu, Dong | - |
dc.date.accessioned | 2022-11-03T02:20:34Z | - |
dc.date.available | 2022-11-03T02:20:34Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015, v. 07-12-June-2015, p. 2774-2783 | - |
dc.identifier.issn | 1063-6919 | - |
dc.identifier.uri | http://hdl.handle.net/10722/321661 | - |
dc.description.abstract | In this work, we formulate a new weakly supervised domain generalization approach for visual recognition by using loosely labeled web images/videos as training data. Specifically, we aim to address two challenging issues when learning robust classifiers: 1) coping with noise in the labels of training web images/videos in the source domain; and 2) enhancing generalization capability of learnt classifiers to any unseen target domain. To address the first issue, we partition the training samples in each class into multiple clusters. By treating each cluster as a 'bag' and the samples in each cluster as 'instances', we formulate a multi-instance learning (MIL) problem by selecting a subset of training samples from each training bag and simultaneously learning the optimal classifiers based on the selected samples. To address the second issue, we assume the training web images/videos may come from multiple hidden domains with different data distributions. We then extend our MIL formulation to learn one classifier for each class and each latent domain such that multiple classifiers from each class can be effectively integrated to achieve better generalization capability. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our new approach for visual recognition by learning from web data. | - |
dc.language | eng | - |
dc.relation.ispartof | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | - |
dc.title | Visual recognition by learning from web data: A weakly supervised domain generalization approach | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/CVPR.2015.7298894 | - |
dc.identifier.scopus | eid_2-s2.0-84959250997 | - |
dc.identifier.volume | 07-12-June-2015 | - |
dc.identifier.spage | 2774 | - |
dc.identifier.epage | 2783 | - |