KGTN-ens: Few-Shot Image Classification with Knowledge Graph Ensembles

6 Nov 2022  ยท  Dominik Filipiak, Anna Fensel, Agata Filipowska ยท

We propose KGTN-ens, a framework extending the recent Knowledge Graph Transfer Network (KGTN) in order to incorporate multiple knowledge graph embeddings at a small cost. We evaluate it with different combinations of embeddings in a few-shot image classification task. We also construct a new knowledge source - Wikidata embeddings - and evaluate it with KGTN and KGTN-ens. Our approach outperforms KGTN in terms of the top-5 accuracy on the ImageNet-FS dataset for the majority of tested settings.

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Datasets


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Few-Shot Image Classification ImageNet-FS (10-shot, all) KGTN-ens (ResNet-50, h+g, max) Top-5 Accuracy (%) 83.46 # 2
Few-Shot Image Classification ImageNet-FS (10-shot, novel) KGTN-ens (ResNet-50, h+g, max) Top-5 Accuracy (%) 82.56 # 1
Few-Shot Image Classification ImageNet-FS (1-shot, all) KGTN-ens (ResNet-50, h+g, max) Top-5 Accuracy (%) 68.58 # 1
Few-Shot Image Classification ImageNet-FS (1-shot, novel) KGTN-ens (ResNet-50, h+g, max) Top-5 Accuracy (%) 62.73 # 1
Few-Shot Image Classification ImageNet-FS (2-shot, all) KGTN-ens (ResNet-50, h+g, max) Top-5 Accuracy (%) 75.45 # 1
Few-Shot Image Classification ImageNet-FS (2-shot, novel) KGTN-ens (ResNet-50, h+g, max) Top-5 Accuracy (%) 71.48 # 1
Few-Shot Image Classification ImageNet-FS (5-shot, all) KGTN-ens (ResNet-50, h+g, max) Top-5 Accuracy (%) 81.12 # 1
Few-Shot Image Classification ImageNet-FS (5-shot, novel) KGTN-ens (ResNet-50, h+g, mean) Top-5 Accuracy (%) 78.90 # 1

Methods


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