Multi-label Zero-shot Classification by Learning to Transfer from External Knowledge

• URL: http://arxiv.org/abs/2007.15610v2
• Date: Fri, 31 Jul 2020 01:29:56 GMT
• Title: Multi-label Zero-shot Classification by Learning to Transfer from External Knowledge
• Authors: He Huang, Yuanwei Chen, Wei Tang, Wenhao Zheng, Qing-Guo Chen, Yao Hu, Philip Yu
• Abstract summary: Multi-label zero-shot classification aims to predict multiple unseen class labels for an input image. This paper introduces a novel multi-label zero-shot classification framework by learning to transfer from external knowledge.
• Score: 36.04579549557464
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-label zero-shot classification aims to predict multiple unseen class labels for an input image. It is more challenging than its single-label counterpart. On one hand, the unconstrained number of labels assigned to each image makes the model more easily overfit to those seen classes. On the other hand, there is a large semantic gap between seen and unseen classes in the existing multi-label classification datasets. To address these difficult issues, this paper introduces a novel multi-label zero-shot classification framework by learning to transfer from external knowledge. We observe that ImageNet is commonly used to pretrain the feature extractor and has a large and fine-grained label space. This motivates us to exploit it as external knowledge to bridge the seen and unseen classes and promote generalization. Specifically, we construct a knowledge graph including not only classes from the target dataset but also those from ImageNet. Since ImageNet labels are not available in the target dataset, we propose a novel PosVAE module to infer their initial states in the extended knowledge graph. Then we design a relational graph convolutional network (RGCN) to propagate information among classes and achieve knowledge transfer. Experimental results on two benchmark datasets demonstrate the effectiveness of the proposed approach.

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