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HOP: History-and-Order Aware Pre-Training for Vision-and-Language Navigation

CVPR 2022 · Vision-language and multimodal › Robot action and navigation · approach: Pretraining and data augmentation for VLN · 8 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Pre-train with PREVALENT and BnB data, using all five proxy tasks (ablation)HOP · REVERIE validation unseenRGSPL: 15.31 -> 15.73Helped
Add MLM pre-training (ablation)HOP · R2R validation unseenSR: 54.19 -> 60.75Helped
Add TIM to MLM pre-training (ablation)HOP · R2R validation unseenSR: 60.75 -> 61.52Helped
Add TOM to MLM pre-training (ablation)HOP · R2R validation unseenSR: 60.75 -> 61.81Helped
Add GOM to MLM pre-training (ablation)HOP · R2R validation unseenSR: 60.75 -> 61.98Helped
Add APH to MLM pre-training (ablation)HOP · R2R validation unseenSR: 60.75 -> 62.01Helped
Add action prediction without history instead of APH (ablation)HOP · R2R validation unseenSR: 62.01 -> 61.27Hurt
Combine TIM, TOM, and GOM with MLM (ablation)HOP · R2R validation unseenSR: 60.75 -> 63.09Helped
Add APH to MLM, TIM, TOM, and GOM (ablation)HOP · R2R validation unseenSR: 63.09 -> 63.86Helped
Remove pre-training (ablation)HOP · R2R validation unseenSR: 63.86 -> 54.19Hurt
Add PREVALENT and BnB* data to pre-training (ablation)HOP · R2R validation unseenSPL: 57.07 -> 57.22Helped
Use HOP pre-training rather than no pre-training (ablation)HOP · R2R test unseenSPL: 50 -> 59Helped

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