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A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks

Wang, Q.; Ning, J.; Breckon, T.P.

A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks Thumbnail


Authors

J. Ning



Abstract

Recent studies on multi-label image classification have focused on designing more complex architectures of deep neural networks such as the use of attention mechanisms and region proposal networks. Although performance gains have been reported, the backbone deep models of the proposed approaches and the evaluation metrics employed in different works vary, making it difficult to compare fairly. Moreover, due to the lack of properly investigated baselines, the advantage introduced by the proposed techniques are often ambiguous. To address these issues, we make a thorough investigation of the mainstream deep convolutional neural network architectures for multi-label image classification and present a strong baseline. With the use of proper data augmentation techniques and model ensembles, the basic deep architectures can achieve better performance than many existing more complex ones on three benchmark datasets, providing great insight for the future studies on multi-label image classification.

Citation

Wang, Q., Ning, J., & Breckon, T. (2019). A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks. In 2019 IEEE International Conference on Image Processing (ICIP) ; proceedings (644-648). https://doi.org/10.1109/icip.2019.8803793

Conference Name 26th IEEE International Conference on Image Processing (ICIP)
Conference Location Taipei, Taiwan
Start Date Sep 22, 2019
End Date Sep 25, 2019
Acceptance Date Apr 30, 2019
Publication Date 2019
Deposit Date Jun 4, 2019
Publicly Available Date Nov 12, 2019
Pages 644-648
Series ISSN 2381-8549
Book Title 2019 IEEE International Conference on Image Processing (ICIP) ; proceedings.
DOI https://doi.org/10.1109/icip.2019.8803793

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