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Research map / Vision-language and multimodal / Robot action and navigation

Think Global, Act Local: Dual-Scale Graph Transformer for Vision-and-Language Navigation

CVPR 2022 · Vision-language and multimodal › Robot action and navigation · approach: Pretraining and data augmentation for VLN · 176 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use dual-scale map and local visual representations with dynamic fusion rather than either scale alone (ablation)DUET · REVERIE val unseenSPL: 25.98 -> 33.73Helped
Replace average fusion with dynamic fusion (ablation)DUET · REVERIE val unseenSPL: 31.94 -> 33.73Helped
Add graph-aware self-attention to dynamic fusion (ablation)DUET · REVERIE val unseenSPL: 32.88 -> 33.73Helped
Add object grounding supervision to SAP training (ablation)DUET · REVERIE val unseenSR: 35.30 -> 37.80Helped
Add MLM and MRC proxy tasks to SAP + OG training (ablation)DUET · REVERIE val unseenRGS: 23.89 -> 25.05Helped
Fine-tune with PID rather than reinforcement learning (ablation)DUET · REVERIE val unseenSR: 42.35 -> 46.98Helped
Add synthetic instructions during pretraining without PID (ablation)DUET · REVERIE val unseenSPL: 25.56 -> 27.19Helped
Add synthetic instruction augmentation when PID is used (ablation)DUET · REVERIE val unseenSPL: 33.73 -> 32.72Hurt
Increase behavior-cloning balance factor from 0.2 to 1 (ablation)DUET · REVERIE val unseenSR: 46.98 -> 45.64Hurt
Remove behavior cloning from fine-tuning (λ = 0) (ablation)DUET · REVERIE val unseenRGSPL: 23.03 -> 22.04Hurt
Use DUET instead of HAMTREVERIE val unseen · REVERIE val unseenSR: 32.95 -> 46.98Helped
Use DUET instead of GBESOON test unseen · SOON test unseenSR: 12.90 -> 33.44Helped

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