3 months ago

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

Dominik Filipiak Anna Fensel Agata Filipowska

Abstract

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.

Code Repositories

DominikFilipiak/KGTN-ens

Official

pytorch

Mentioned in GitHub

Benchmarks

Benchmark	Methodology	Metrics
few-shot-image-classification-on-imagenet-fs	KGTN-ens (ResNet-50, h+g, max)	Top-5 Accuracy (%): 62.73
few-shot-image-classification-on-imagenet-fs-1	KGTN-ens (ResNet-50, h+g, max)	Top-5 Accuracy (%): 71.48
few-shot-image-classification-on-imagenet-fs-2	KGTN-ens (ResNet-50, h+g, mean)	Top-5 Accuracy (%): 78.90
few-shot-image-classification-on-imagenet-fs-3	KGTN-ens (ResNet-50, h+g, max)	Top-5 Accuracy (%): 82.56
few-shot-image-classification-on-imagenet-fs-4	KGTN-ens (ResNet-50, h+g, max)	Top-5 Accuracy (%): 68.58
few-shot-image-classification-on-imagenet-fs-5	KGTN-ens (ResNet-50, h+g, max)	Top-5 Accuracy (%): 75.45
few-shot-image-classification-on-imagenet-fs-6	KGTN-ens (ResNet-50, h+g, max)	Top-5 Accuracy (%): 81.12
few-shot-image-classification-on-imagenet-fs-7	KGTN-ens (ResNet-50, h+g, max)	Top-5 Accuracy (%): 83.46

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