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Enhance Via Decoupling: Improving Multi-Label Classifiers With Variational Feature Augmentation

Chen, M; Wang, G; Xue, J-H; Ding, Z; Sun, L; (2021) Enhance Via Decoupling: Improving Multi-Label Classifiers With Variational Feature Augmentation. In: Proceedings of the IEEE International Conference on Image Processing (ICIP) 2021. (pp. pp. 1329-1333). Institute of Electrical and Electronics Engineers (IEEE) Green open access

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Abstract

Multi-label classification remains a challenging problem due to the inherent label imbalance issue, which brings overfitting of minor categories to modern deep models. In this paper, to tackle this issue, we propose a novel method named Variational Feature Augmentation (VFA) to enhance the deep neural networks for multi-label classification. Our method decouples the feature vectors extracted by the backbone network into multiple low-dimensional spaces via a novely proposed Variational Feature Decoupling Module. The decoupled feature vectors are then re-combined with a shuffle operation and a Feature Augmentation Layer to enrich the minor co-occurrence relations, mitigating the label imbalance. Different from most other methods, VFA does not modify the network architecture or introduce extra computation cost in inference phase. We conduct comprehensive experiments on four benchmarks of two visual multi-label classification tasks, pedestrian attribute recognition and multi-label image recognition, and the results demonstrate the effectiveness and generality of the proposed VFA.

Type: Proceedings paper
Title: Enhance Via Decoupling: Improving Multi-Label Classifiers With Variational Feature Augmentation
Event: 2021 IEEE International Conference on Image Processing (ICIP)
Location: Anchorage (AK), USA
Dates: 19th-22nd September 2021
ISBN-13: 978-1-6654-4115-5
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/icip42928.2021.9506370
Publisher version: https://doi.org/10.1109/ICIP42928.2021.9506370
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
Keywords: Deep Learning, Multi-Label Classification, Pedestrian Attribute Recognition
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery-pp.ucl.ac.uk/id/eprint/10133572
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