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Disentangling Foreground and Background for vision-Language Navigation via Online Augmentation

arXiv 2510.00604 ↗First posted 1 Oct 2025cs.CVcited by 0

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Abstract (arXiv)

Following language instructions, vision-language navigation (VLN) agents are tasked with navigating unseen environments. While augmenting multifaceted visual representations has propelled advancements in VLN, the significance of foreground and background in visual observations remains underexplored. Intuitively, foreground regions provide semantic cues, whereas the background encompasses spatial connectivity information. Inspired on this insight, we propose a Consensus-driven Online Feature Augmentation strategy (COFA) with alternative foreground and background features to facilitate the navigable generalization. Specifically, we first leverage semantically-enhanced landmark identification to disentangle foreground and background as candidate augmented features. Subsequently, a consensus-driven online augmentation strategy encourages the agent to consolidate two-stage voting results on feature preferences according to diverse instructions and navigational locations. Experiments on REVERIE and R2R demonstrate that our online foreground-background augmentation boosts the generalization of baseline and attains state-of-the-art performance.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 23 references that are in the map. First posted on arXiv 1 Oct 2025.

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References · 17

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2025Qwen2.5-VL Technical Reportcited by 5,418
  3. 2020MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformerscited by 1,414
  4. 2017Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environmentscited by 1,013
  5. 2019REVERIE: Remote Embodied Visual Referring Expression in Real Indoor Environmentscited by 330
  6. 2021History Aware Multimodal Transformer for Vision-and-Language Navigationcited by 268
  7. 2022Think Global, Act Local: Dual-scale Graph Transformer for Vision-and-Language Navigationcited by 187
  8. 2022BEVBert: Multimodal Map Pre-training for Language-guided Navigationcited by 117
  9. 2023GridMM: Grid Memory Map for Vision-and-Language Navigationcited by 117
  10. 2023Scaling Data Generation in Vision-and-Language Navigationcited by 115
  11. 2022EnvEdit: Environment Editing for Vision-and-Language Navigationcited by 83
  12. 2023PanoGen: Text-Conditioned Panoramic Environment Generation for Vision-and-Language Navigationcited by 77
5 more
  1. 2023Open-Set Image Tagging with Multi-Grained Text Supervisioncited by 67
  2. 2024EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything Modelcited by 63
  3. 2023A Dual Semantic-Aware Recurrent Global-Adaptive Network For Vision-and-Language Navigationcited by 18
  4. 2025Unseen from Seen: Rewriting Observation-Instruction Using Foundation Models for Augmenting Vision-Language Navigationcited by 8
  5. 2023DAP: Domain-aware Prompt Learning for Vision-and-Language Navigationcited by 4

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