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Scene-Intuitive Agent for Remote Embodied Visual Grounding

CVPR 2021 · Vision-language and multimodal › Robot action and navigation · approach: Pretraining and data augmentation for VLN · 41 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the Scene Grounding-pretrained ViL encoder instead of a raw ViL encoder (ablation)REVERIE navigation agent · REVERIE Val Seen and Val UnseenRG SPL: 21.94 -> 33.92; 4.84 -> 7.31Helped
Replace MattNet with ViLPointer (ablation)REVERIE navigation agent · REVERIE Val Seen and Val UnseenNavigation Length: 14.56 -> 13.71; 42.62 -> 37.31Helped
Replace the conventional policy with the memory-augmented policy (ablation)REVERIE navigation agent · REVERIE Val Seen and Val UnseenRGS: 23.82 -> 24.88; 8.89 -> 11.08Helped
Use memory and state block counts (3, 3) rather than (1, 1) (ablation)Memory-augmented action decoder · REVERIE Val Seen and Val UnseenRG SPL: 38.76 -> 42.65; 9.19 -> 11.56Helped
Increase memory and state block counts from (3, 3) to (5, 5) (ablation)Memory-augmented action decoder · REVERIE Val SeenRG SPL: 42.65 -> 41.10Hurt
Fuse action, scene-grounding, and object-grounding logits (ablation)REVERIE navigation agent · REVERIE Val Seen and Val UnseenRG SPL: 42.19 -> 42.65; 10.54 -> 11.56Helped
Use viewpoint-based rather than image-based object grounding (ablation)ViLPointer · REVERIE Val Seen and Val Unseenobject grounding accuracy: 65.72 -> 73.26; 55.53 -> 67.45Helped
Use the complete scene-intuitive agent rather than REVERIE baselineREVERIE · REVERIE Val Seen, Val Unseen, and Test (Unseen)RG SPL: 29.66 -> 42.65; 4.67 -> 11.56; 6.08 -> 9.20Helped

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