Instruction understanding is reframed as an evolving Instruction-as-State variable conditioned on perceptual state and realized via the S-EGIU coarse-to-fine framework, reporting a +2.68% SPL gain on REVERIE Test Unseen.
TRAVEL: Training-Free Retrieval and Alignment for Vision-and-Language Navigation
2 Pith papers cite this work. Polarity classification is still indexing.
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A training-free inference layer that reweights stale memory and penalizes immediate reversals improves trajectory efficiency and keeps or slightly improves success on R2R and REVERIE with a GridMM-based navigator.
citing papers explorer
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Instruction-as-State: Environment-Guided and State-Conditioned Semantic Understanding for Embodied Navigation
Instruction understanding is reframed as an evolving Instruction-as-State variable conditioned on perceptual state and realized via the S-EGIU coarse-to-fine framework, reporting a +2.68% SPL gain on REVERIE Test Unseen.
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DART-VLN: Test-Time Memory Decay and Anti-Loop Regularization for Discrete Vision-Language Navigation
A training-free inference layer that reweights stale memory and penalizes immediate reversals improves trajectory efficiency and keeps or slightly improves success on R2R and REVERIE with a GridMM-based navigator.