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Semantic-Aligned Fusion Transformer for One-Shot Object Detection

CVPR 2022 · Detection and segmentation › CNN features · approach: Context for hard detection conditions · 32 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add semantic-aligned VFM and HFM fusionFCOS one-shot detection · PASCAL-VOC 2007 test, Novel Avg.AP50: 74.5 vs 73.1Helped
Add semantic-aligned VFM and HFM fusionFCOS one-shot detection · MS-COCO 2017 val, Novel Avg.AP50: 24.9 vs 24.3Helped
Add HFM instead of reweighting or correlation (ablation)VOC detector with VFM · PASCAL-VOC 2007 test, NovelAP50: 64.2 -> 71.7; 64.3 -> 71.7Helped
Replace FPN with VFM (ablation)VOC detector with HFM · PASCAL-VOC 2007 test, NovelAP50: 69.2 -> 71.7Helped
Use corresponding-scale rather than one-to-all-scale fusion (ablation)SaFT with VFM and HFM · PASCAL-VOC 2007 test, NovelAP50: 69.9 -> 71.7Hurt
Add level 5 to corresponding-scale feature fusion (ablation)SaFT · PASCAL-VOC 2007 test, NovelAP50: 61.4 -> 70.0Helped
Add level 6 to corresponding-scale feature fusion (ablation)SaFT · PASCAL-VOC 2007 test, NovelAP50: 70.0 -> 69.9Hurt
Use level 5 rather than level 4 as the only support feature (ablation)SaFT one-to-all-scale fusion · PASCAL-VOC 2007 test, NovelAP50: 68.1 -> 71.7Helped
Increase HA blocks from 2 to 4 (ablation)SaFT · PASCAL-VOC 2007 test, NovelAP50: 69.5 -> 71.7Helped
Increase HA blocks from 4 to 6 (ablation)SaFT · PASCAL-VOC 2007 test, NovelAP50: 71.7 -> 72.8Helped

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