SpaAct: 空間活性化遷移学習とカリキュラム適応による視覚言語ナビゲーション
SpaAct: Spatially-Activated Transition Learning with Curriculum Adaptation for Vision-Language Navigation
視覚言語ナビゲーション(VLN)のためのVLM適応フレームワークSpaActを提案。後方行動推論と前方遷移予測の2つの空間活性化タスクと、TriPAカリキュラム学習により動的空間認識を獲得し、VLN-CEベンチマークでSOTAを達成。
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著者: Pengna Li, Kangyi Wu, Shaoqing Xu, Fang Li, Hanbing Li, Lin Zhao, Kailin Lyu, Long Chen, Zhi-Xin Yang, Nanning Zheng
分類: cs.CV
原文アブストラクト
Vision-and-Language Navigation (VLN) aims to enable an embodied agent to follow natural-language instructions and navigate to a target location in unseen 3D environments. We argue that adapting VLMs to VLN requires endowing them with two complementary capabilities for acquiring such awareness, namely backward action reasoning (why) and forward transition prediction~(how). Based on this insight, we propose SpaAct, a simple yet effective training framework that activates the dynamic spatial awareness in VLMs. Specifically, SpaAct introduces two spatial activation tasks: Action Retrospection, which asks the model to infer the executed action sequence from visual transitions, and Future Frame Selection, which forces the model to predict the visual transitions conditioned on history and action. These two objectives provide lightweight supervision on both backward action reasoning and forward transition prediction, encouraging the model to build dynamic spatial awareness in a VLM-friendly way. To further stabilize adaptation, we design TriPA, a Tri-factor Progressive Adaptive curriculum learning method that organizes training samples from easy to hard, allowing the model to gradually acquire navigation skills from basic locomotion to long-horizon reasoning. Experiments on standard VLN-CE benchmarks show that SpaAct consistently improves VLM-based navigation and achieves state-of-the-art performance. We will release the code and models to support future research.
