mlsanityData to 7 Oct 2026

Semantic Enhanced Knowledge Graph for Large-Scale Zero-Shot Learning

arXiv 2212.13151 ↗First posted 26 Dec 2022cs.CVcited by 2

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Abstract (arXiv)

Zero-Shot Learning has been a highlighted research topic in both vision and language areas. Recently, most existing methods adopt structured knowledge information to model explicit correlations among categories and use deep graph convolutional network to propagate information between different categories. However, it is difficult to add new categories to existing structured knowledge graph, and deep graph convolutional network suffers from over-smoothing problem. In this paper, we provide a new semantic enhanced knowledge graph that contains both expert knowledge and categories semantic correlation. Our semantic enhanced knowledge graph can further enhance the correlations among categories and make it easy to absorb new categories. To propagate information on the knowledge graph, we propose a novel Residual Graph Convolutional Network (ResGCN), which can effectively alleviate the problem of over-smoothing. Experiments conducted on the widely used large-scale ImageNet-21K dataset and AWA2 dataset show the effectiveness of our method, and establish a new state-of-the-art on zero-shot learning. Moreover, our results on the large-scale ImageNet-21K with various feature extraction networks show that our method has better generalization and robustness.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 44 of its 83 references that are in the map. First posted on arXiv 26 Dec 2022.

Builds on

44 of 83 references.

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32 more
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