Papers › KGTN-ens: Few-Shot Image Classification with Knowledge Graph Ensembles
KGTN-ens: Few-Shot Image Classification with Knowledge Graph Ensembles
Dominik Filipiak, Anna Fensel, Agata Filipowska
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
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Few-Shot Image Classification | ImageNet-FS (1-shot, all) | KGTN-ens (ResNet-50, h+g, max) | Top-5 Accuracy (%) | 68.58 | #1 of 2 | Archive leaderboard | report |
| Few-Shot Image Classification | ImageNet-FS (1-shot, novel) | KGTN-ens (ResNet-50, h+g, max) | Top-5 Accuracy (%) | 62.73 | #1 of 7 | Archive leaderboard | report |
| Few-Shot Image Classification | ImageNet-FS (10-shot, all) | KGTN-ens (ResNet-50, h+g, max) | Top-5 Accuracy (%) | 83.46 | #2 of 2 | Archive leaderboard | report |
| Few-Shot Image Classification | ImageNet-FS (10-shot, novel) | KGTN-ens (ResNet-50, h+g, max) | Top-5 Accuracy (%) | 82.56 | #1 of 2 | Archive leaderboard | report |
| Few-Shot Image Classification | ImageNet-FS (2-shot, all) | KGTN-ens (ResNet-50, h+g, max) | Top-5 Accuracy (%) | 75.45 | #1 of 2 | Archive leaderboard | report |
| Few-Shot Image Classification | ImageNet-FS (2-shot, novel) | KGTN-ens (ResNet-50, h+g, max) | Top-5 Accuracy (%) | 71.48 | #1 of 8 | Archive leaderboard | report |
| Few-Shot Image Classification | ImageNet-FS (5-shot, all) | KGTN-ens (ResNet-50, h+g, max) | Top-5 Accuracy (%) | 81.12 | #1 of 8 | Archive leaderboard | report |
| Few-Shot Image Classification | ImageNet-FS (5-shot, novel) | KGTN-ens (ResNet-50, h+g, mean) | Top-5 Accuracy (%) | 78.90 | #1 of 3 | Archive leaderboard | report |
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