Knowledge Fused Recognition: Fusing Hierarchical Knowledge for Image Recognition through Quantitative Relativity Modeling and Deep Metric Learning

• URL: http://arxiv.org/abs/2407.20600v1
• Date: Tue, 30 Jul 2024 07:24:33 GMT
• Title: Knowledge Fused Recognition: Fusing Hierarchical Knowledge for Image Recognition through Quantitative Relativity Modeling and Deep Metric Learning
• Authors: Yunfeng Zhao, Huiyu Zhou, Fei Wu, Xifeng Wu,
• Abstract summary: We propose a novel deep metric learning based method to fuse hierarchical prior knowledge about image classes. Existing deep metric learning incorporated image classification mainly exploits qualitative relativity between image classes. A new triplet loss function term that exploits quantitative relativity and aligns distances in model latent space with those in knowledge space is also proposed and incorporated in the proposed dual-modality fusion method.
• Score: 18.534970504136254
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image recognition is an essential baseline for deep metric learning. Hierarchical knowledge about image classes depicts inter-class similarities or dissimilarities. Effective fusion of hierarchical knowledge about image classes to enhance image recognition remains a challenging topic to advance. In this paper, we propose a novel deep metric learning based method to effectively fuse hierarchical prior knowledge about image classes and enhance image recognition performances in an end-to-end supervised regression manner. Existing deep metric learning incorporated image classification mainly exploits qualitative relativity between image classes, i.e., whether sampled images are from the same class. A new triplet loss function term that exploits quantitative relativity and aligns distances in model latent space with those in knowledge space is also proposed and incorporated in the proposed dual-modality fusion method. Experimental results indicate that the proposed method enhanced image recognition performances and outperformed baseline and existing methods on CIFAR-10, CIFAR-100, Mini-ImageNet, and ImageNet-1K datasets.

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