Object-Aware Self-supervised Multi-Label Learning

• URL: http://arxiv.org/abs/2205.07028v1
• Date: Sat, 14 May 2022 10:14:08 GMT
• Title: Object-Aware Self-supervised Multi-Label Learning
• Authors: Xu Kaixin, Liu Liyang, Zhao Ziyuan, Zeng Zeng, Bharadwaj Veeravalli
• Abstract summary: We propose an Object-Aware Self-Supervision (OASS) method to obtain more fine-grained representations for multi-label learning. The proposed method can be leveraged to efficiently generate Class-Specific Instances (CSI) in a proposal-free fashion. Experiments on the VOC2012 dataset for multi-label classification demonstrate the effectiveness of the proposed method against the state-of-the-art counterparts.
• Score: 9.496981642855769
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-label Learning on Image data has been widely exploited with deep learning models. However, supervised training on deep CNN models often cannot discover sufficient discriminative features for classification. As a result, numerous self-supervision methods are proposed to learn more robust image representations. However, most self-supervised approaches focus on single-instance single-label data and fall short on more complex images with multiple objects. Therefore, we propose an Object-Aware Self-Supervision (OASS) method to obtain more fine-grained representations for multi-label learning, dynamically generating auxiliary tasks based on object locations. Secondly, the robust representation learned by OASS can be leveraged to efficiently generate Class-Specific Instances (CSI) in a proposal-free fashion to better guide multi-label supervision signal transfer to instances. Extensive experiments on the VOC2012 dataset for multi-label classification demonstrate the effectiveness of the proposed method against the state-of-the-art counterparts.

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