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Article: P-CNN: Part-Based Convolutional Neural Networks for Fine-Grained Visual Categorization

TitleP-CNN: Part-Based Convolutional Neural Networks for Fine-Grained Visual Categorization
Authors
Keywordsduplex focal loss
fine-grained visual categorization
part classification network
Part localization network
Issue Date2022
Citation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, v. 44, n. 2, p. 579-590 How to Cite?
AbstractThis paper proposes an end-to-end fine-grained visual categorization system, termed Part-based Convolutional Neural Network (P-CNN), which consists of three modules. The first module is a Squeeze-and-Excitation (SE) block, which learns to recalibrate channel-wise feature responses by emphasizing informative channels and suppressing less useful ones. The second module is a Part Localization Network (PLN) used to locate distinctive object parts, through which a bank of convolutional filters are learned as discriminative part detectors. Thus, a group of informative parts can be discovered by convolving the feature maps with each part detector. The third module is a Part Classification Network (PCN) that has two streams. The first stream classifies each individual object part into image-level categories. The second stream concatenates part features and global feature into a joint feature for the final classification. In order to learn powerful part features and boost the joint feature capability, we propose a Duplex Focal Loss used for metric learning and part classification, which focuses on training hard examples. We further merge PLN and PCN into a unified network for an end-to-end training process via a simple training technique. Comprehensive experiments and comparisons with state-of-the-art methods on three benchmark datasets demonstrate the effectiveness of our proposed method.
Persistent Identifierhttp://hdl.handle.net/10722/321978
ISSN
2023 Impact Factor: 20.8
2023 SCImago Journal Rankings: 6.158
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorHan, Junwei-
dc.contributor.authorYao, Xiwen-
dc.contributor.authorCheng, Gong-
dc.contributor.authorFeng, Xiaoxu-
dc.contributor.authorXu, Dong-
dc.date.accessioned2022-11-03T02:22:46Z-
dc.date.available2022-11-03T02:22:46Z-
dc.date.issued2022-
dc.identifier.citationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, v. 44, n. 2, p. 579-590-
dc.identifier.issn0162-8828-
dc.identifier.urihttp://hdl.handle.net/10722/321978-
dc.description.abstractThis paper proposes an end-to-end fine-grained visual categorization system, termed Part-based Convolutional Neural Network (P-CNN), which consists of three modules. The first module is a Squeeze-and-Excitation (SE) block, which learns to recalibrate channel-wise feature responses by emphasizing informative channels and suppressing less useful ones. The second module is a Part Localization Network (PLN) used to locate distinctive object parts, through which a bank of convolutional filters are learned as discriminative part detectors. Thus, a group of informative parts can be discovered by convolving the feature maps with each part detector. The third module is a Part Classification Network (PCN) that has two streams. The first stream classifies each individual object part into image-level categories. The second stream concatenates part features and global feature into a joint feature for the final classification. In order to learn powerful part features and boost the joint feature capability, we propose a Duplex Focal Loss used for metric learning and part classification, which focuses on training hard examples. We further merge PLN and PCN into a unified network for an end-to-end training process via a simple training technique. Comprehensive experiments and comparisons with state-of-the-art methods on three benchmark datasets demonstrate the effectiveness of our proposed method.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Pattern Analysis and Machine Intelligence-
dc.subjectduplex focal loss-
dc.subjectfine-grained visual categorization-
dc.subjectpart classification network-
dc.subjectPart localization network-
dc.titleP-CNN: Part-Based Convolutional Neural Networks for Fine-Grained Visual Categorization-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TPAMI.2019.2933510-
dc.identifier.pmid31398107-
dc.identifier.scopuseid_2-s2.0-85122800249-
dc.identifier.volume44-
dc.identifier.issue2-
dc.identifier.spage579-
dc.identifier.epage590-
dc.identifier.eissn1939-3539-
dc.identifier.isiWOS:000740006100004-

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