クエリ・アライメント・蒸留：効率的な視覚言語ナビゲーションのためのナビゲーション認識クロスモーダル相互作用
Query, Align, and Distill: Navigation-Aware Cross-Modal Interaction for Efficient Vision-and-Language Navigation
視覚言語ナビゲーション（VLN）において、ナビゲーション証拠を明示的に選択する教師モデルを構築し、そのクエリを蒸留することで、パラメータ数を93.65%削減しつつ性能をほぼ維持するコンパクトな生徒モデルを実現した。
著者: Zhihao Chen, Yiyuan Ge, Ziyang Wang, Pu Cao, Lu Yang
分類: cs.CV, cs.AI, cs.RO
原文アブストラクト
Recent large-scale Vision-and-Language Navigation (VLN) models deliver strong accuracy but remain costly to deploy due to their large parameter counts and computational requirements. We tackle efficient VLN in two steps. First, we build a high-performing teacher that makes navigation evidence selection explicit and compressible. The teacher introduces a small set of learnable query slots to extract global and local action-sufficient navigable evidence from panoramic observations via a Navigable Query Generator, then progressively grounds these evidence tokens to the instruction with an Instruction-Query Aligner for policy prediction. Second, using this explicit query bottleneck as a distillation interface, we train a compact student by transferring both where to attend and what to do. We distill the teacher's global and local navigable queries with a navigation-aware token-adaptive objective, then further match action distributions during fine-tuning. Experiments on standard VLN benchmarks demonstrate that our student nearly matches the teacher's navigation performance while reducing the number of parameters by 93.65% compared to the teacher.