Journal
IEEE ACCESS
Volume 7, Issue -, Pages 98005-98013Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2929512
Keywords
Deep neural network; multi-label recognition; label correlation; attention
Categories
Funding
- Natural Science Foundation of China [61673187]
- Shenzhen Innovative, Research Institute in Shenzhen [JCYJ20170307160806070]
- Huazhong University of Science and Technology
- NPRP through the Qatar National Research Fund [NPRP 9-466-1-103]
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Learning the correlation among labels is a standing-problem in the multi-label image recognition task. The label correlation is the key to solve the multi-label classification but it is too abstract to model. Most solutions try to learn image label dependencies to improve multi-label classification performance. However, they have ignored two more realistic problems: object scale inconsistent and label tail (category imbalance). These two problems will impact the bad influence on the classification model. To tackle these two problems and learn the label correlations, we propose feature attention network (FAN) which contains feature refinement network and correlation learning network. FAN builds top-down feature fusion mechanism to refine more important features and learn the correlations among convolutional features from FAN to indirect learn the label dependencies. Following our proposed solution, we achieve performed classification accuracy on MSCOCO 2014 and VOC 2007 dataset.
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