Abstract
Zero-shot learning (ZSL) is a powerful and promising learning paradigm for classifying instances that have not been seen in training. Although graph convolutional networks (GCNs) have recently shown great potential for the ZSL tasks, these models cannot adjust the constant connection weights between the nodes in knowledge graph and the neighbor nodes contribute equally to classify the central node. In this study, we apply an attention mechanism to adjust the connection weights adaptively to learn more important information for classifying unseen target nodes. First, we propose an attention graph convolutional network for zero-shot learning (AGCNZ) by integrating the attention mechanism and GCN directly. Then, in order to prevent the dilution of knowledge from distant nodes, we apply the dense graph propagation (DGP) model for the ZSL tasks and propose an attention dense graph propagation model for zero-shot learning (ADGPZ). Finally, we propose a modified loss function with a relaxation factor to further improve the performance of the learned classifier. Experimental results under different pre-training settings verified the effectiveness of the proposed attention-based models for ZSL.
Highlights
Image classification can be viewed as the task to correctly classify the given image into its class. ere are many supervised models that have achieved significant success in image classification, such as K-nearest neighbors (KNN) [1] and support vector machines (SVM) [2]
Similar performances can be found for the aPY testing set. e classification accuracy of AGCNZl and AGDPZl( 66.8%, 50.8%, 65.6% and 48.3%) is better than that of the baseline method
Without the modified loss function, the accuracy of ADGPZ classification was improved by 3% over the baseline method. e attention mechanism introduced in the baseline method is significantly better than the baseline method as exhibited in Tables 2 and 3
Summary
Image classification can be viewed as the task to correctly classify the given image into its class. ere are many supervised models that have achieved significant success in image classification, such as K-nearest neighbors (KNN) [1] and support vector machines (SVM) [2]. Image classification can be viewed as the task to correctly classify the given image into its class. Ere are many supervised models that have achieved significant success in image classification, such as K-nearest neighbors (KNN) [1] and support vector machines (SVM) [2]. In recent years, deep learning techniques have made great progress in image classification. Ere are about 30,000 classes that humans can recognize [3], where the workload is quite huge to label all classes and the classes may be growing over time. Humans are very good at recognizing the unseen classes via reasoning. It is important for the agents to acquire the ability of recognizing the unseen classes and zero-shot learning (ZSL) is proposed
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