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Do Visual Imaginations Improve Vision-and-Language Navigation Agents?
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Generates text-to-image "visual imaginations" of instruction landmarks with SDXL and feeds them as an extra modality with a cosine alignment loss into HAMT and DUET VLN agents, giving small gains on R2R and REVERIE.
Problem
Vision-and-language navigation agents rely on implicit cross-modal alignment to ground landmark noun phrases in instructions, and grounding remains weak, especially for out-of-distribution landmarks. The paper asks whether visual representations of sub-goals implied by instructions improve navigatio
Approach
Segment R2R instructions into sub-instructions, filter for landmark noun phrases, synthesize images of them with SDXL ("imaginations"), encode them with a ViT plus MLP and inject them as extra tokens concatenated with text embeddings, and add a cosine loss aligning imagination embeddings with noun-phrase text embeddings. The method is agent-agnostic and is applied via finetuning of existing checkp
Key results
- HAMT-Imagine improves R2R val-unseen SR from 66.24 to 67.26 and SPL from 61.51 to 62.02.
- DUET-Imagine improves REVERIE val-unseen SR from 46.98 to 48.28 and RGS from 32.15 to 32.97.
- Wrong (mismatched) imaginations at test time drop HAMT-Imagine to baseline SR 66.24, indicating alignment matters.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add visual imaginations and alignment loss | HAMT · R2R val-unseen | SR: 66.24 -> 67.26 | Helped |
| add visual imaginations and alignment loss | DUET · R2R val-unseen | SR: 71.52 -> 72.12 | Helped |
| add visual imaginations and alignment loss | DUET · REVERIE | SR: 46.98 -> 48.28 | Helped |
| set imagination attention masks to zero at test time (ablation) | HAMT-Imagine · R2R val-unseen | SR: 67.26 -> 66.92 | Hurt |
| use imaginations sampled from different instructions (ablation) | HAMT-Imagine · R2R val-unseen | SR: 67.26 -> 66.24 | Hurt |
| provide only the final valid sub-instruction imagination instead of sequential imaginations (ablation) | HAMT-Imagine · R2R val-unseen | SR: 67.26 -> 66.79 | Hurt |
| use a fine-tuned ViT rather than an off-the-shelf pretrained ViT (ablation) | HAMT-Imagine and DUET-Imagine · R2R val-unseen | SR: 67.31 vs. 67.26; 72.58 vs. 72.12 | No clear effect |
| remove the auxiliary alignment loss (ablation) | HAMT-Imagine · R2R val-unseen | SR: 66.75 -> 67.26 | Hurt |
| replace cosine alignment loss with InfoNCE loss (ablation) | HAMT-Imagine · R2R val-unseen | SR: 67.18 vs. 67.26 | No clear effect |
Surprising findings
- Test-time null imaginations still beat baseline, suggesting a regularization effect from imagination-based training.
- Off-the-shelf ViT performs comparably to a ViT fine-tuned in-the-loop for encoding imaginations.
Limitations
- Generating and encoding imaginations adds computational cost for running VLN agents on-device.
- Imaginations are not grounded in the specific environment, so personalized or unique object/location names cannot be imagined.
- Gains are modest (about 1 SR and 0.5 SPL on R2R).
Experimental setup
Datasets: R2R REVERIE FG-R2R Matterport3D
Benchmarks: R2R REVERIE
Metrics: SR SPL NE TL RGS RGSPL
Baselines: HAMT DUET PREVALENT RecBERT ADAPT BEVBert MARVAL ScaleVLN Pathdreamer PanoGen VLN-Sig LAD
Builds on: HAMT DUET FG-R2R SDXL ADAPT
Closest papers by meaning
- Planning from Imagination: Episodic Simulation and Episodic Memory for Vision-and-Language Navigation (AAAI 2025 · similarity 0.91)
- ImagineNav: Prompting Vision-Language Models as Embodied Navigator through Scene Imagination (ICLR 2025 · similarity 0.88)
- NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language Models (AAAI 2024 · similarity 0.88)
- FantasyVLN: Unified Multimodal Chain-of-Thought Reasoning for Vision-and-Language Navigation (CVPR 2026 · similarity 0.87)
- Thinking with Imagination: Agentic Visual Spatial Reasoning with World Simulators (NeurIPS 2026 · similarity 0.87)
- VELMA: Verbalization Embodiment of LLM Agents for Vision and Language Navigation in Street View (AAAI 2024 · similarity 0.87)
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