Learning Discriminative Representations for Multi-Label Image Recognition

• URL: http://arxiv.org/abs/2107.11159v1
• Date: Fri, 23 Jul 2021 12:10:46 GMT
• Title: Learning Discriminative Representations for Multi-Label Image Recognition
• Authors: Mohammed Hassanin, Ibrahim Radwan, Salman Khan, Murat Tahtali
• Abstract summary: We propose a unified deep network to learn discriminative features for the multi-label task. By regularizing the whole network with the proposed loss, the performance of applying the wellknown ResNet-101 is improved significantly.
• Score: 13.13795708478267
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-label recognition is a fundamental, and yet is a challenging task in computer vision. Recently, deep learning models have achieved great progress towards learning discriminative features from input images. However, conventional approaches are unable to model the inter-class discrepancies among features in multi-label images, since they are designed to work for image-level feature discrimination. In this paper, we propose a unified deep network to learn discriminative features for the multi-label task. Given a multi-label image, the proposed method first disentangles features corresponding to different classes. Then, it discriminates between these classes via increasing the inter-class distance while decreasing the intra-class differences in the output space. By regularizing the whole network with the proposed loss, the performance of applying the wellknown ResNet-101 is improved significantly. Extensive experiments have been performed on COCO-2014, VOC2007 and VOC2012 datasets, which demonstrate that the proposed method outperforms state-of-the-art approaches by a significant margin of 3:5% on large-scale COCO dataset. Moreover, analysis of the discriminative feature learning approach shows that it can be plugged into various types of multi-label methods as a general module.

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