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GeoLayoutLM: Geometric Pre-Training for Visual Information Extraction

CVPR 2023 · Vision-language and multimodal › Human and embodied modeling · approach: Layout-aware document understanding · 53 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add geometric pre-training to document pre-training (ablation)GeoLayoutLM · FUNSD REF1: 74.91 -> 85.32Helped
add GeoPair geometric pre-training (ablation)GeoLayoutLM · FUNSD SERSER: 83.39 -> 91.80Helped
add GeoMPair geometric pre-training (ablation)GeoLayoutLM · FUNSD RERE: 74.91 -> 82.56Helped
add GeoTriplet geometric pre-training (ablation)GeoLayoutLM · FUNSD RERE: 74.91 -> 78.90Helped
combine GeoPair and GeoMPair pre-training (ablation)GeoLayoutLM · FUNSD RERE: 82.23 -> 85.22Helped
combine all three geometric pre-training tasks (ablation)GeoLayoutLM · FUNSDSER and RE: 91.86 -> 92.17; 85.22 -> 85.32Helped
pre-train the CRP head (ablation)GeoLayoutLM · FUNSD REF1: 82.2 -> 84.9Helped
add an unpretrained RFE head to pre-trained CRP (ablation)GeoLayoutLM · FUNSD REF1: 84.9 -> 84.0Hurt
pre-train the RFE head (ablation)GeoLayoutLM · FUNSD REF1: 84.0 -> 86.2Helped
pre-train both CRP and RFE heads (ablation)GeoLayoutLM · FUNSD REF1: 84.0 -> 86.9Helped
add RSF postprocessing (ablation)GeoLayoutLM · FUNSD REF1: 87.64 -> 88.62Helped
add RSF variance loss without postprocessing (ablation)GeoLayoutLM · FUNSD REF1: 87.64 -> 87.62No clear effect

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