Data Augmentation Imbalance For Imbalanced Attribute Classification

• URL: http://arxiv.org/abs/2004.13628v3
• Date: Thu, 21 May 2020 07:36:49 GMT
• Title: Data Augmentation Imbalance For Imbalanced Attribute Classification
• Authors: Yang Hu, Xiaying Bai, Pan Zhou, Fanhua Shang, Shengmei Shen
• Abstract summary: We propose a new re-sampling algorithm called: data augmentation imbalance (DAI) to explicitly enhance the ability to discriminate the fewer attributes. Our DAI algorithm achieves state-of-the-art results, based on pedestrian attribute datasets.
• Score: 60.71438625139922
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pedestrian attribute recognition is an important multi-label classification problem. Although the convolutional neural networks are prominent in learning discriminative features from images, the data imbalance in multi-label setting for fine-grained tasks remains an open problem. In this paper, we propose a new re-sampling algorithm called: data augmentation imbalance (DAI) to explicitly enhance the ability to discriminate the fewer attributes via increasing the proportion of labels accounting for a small part. Fundamentally, by applying over-sampling and under-sampling on the multi-label dataset at the same time, the thought of robbing the rich attributes and helping the poor makes a significant contribution to DAI. Extensive empirical evidence shows that our DAI algorithm achieves state-of-the-art results, based on pedestrian attribute datasets, i.e. standard PA-100K and PETA datasets.

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