視覚と言語のナビゲーションのための転移可能な表現学習
Transferable Representation Learning in Vision-and-Language Navigation
視覚と言語のナビゲーション(VLN)向けに、事前学習済みの視覚・言語表現をクロスモーダルな系列アライメントと系列コヒーレンスのタスクで適応させ、R2RベンチマークのSPLを改善した研究。
著者: Haoshuo Huang, Vihan Jain, Harsh Mehta, Alexander Ku, Gabriel Magalhaes, Jason Baldridge, Eugene Ie
分類: cs.CV, cs.CL, cs.LG, cs.RO
原文アブストラクト
Vision-and-Language Navigation (VLN) tasks such as Room-to-Room (R2R) require machine agents to interpret natural language instructions and learn to act in visually realistic environments to achieve navigation goals. The overall task requires competence in several perception problems: successful agents combine spatio-temporal, vision and language understanding to produce appropriate action sequences. Our approach adapts pre-trained vision and language representations to relevant in-domain tasks making them more effective for VLN. Specifically, the representations are adapted to solve both a cross-modal sequence alignment and sequence coherence task. In the sequence alignment task, the model determines whether an instruction corresponds to a sequence of visual frames. In the sequence coherence task, the model determines whether the perceptual sequences are predictive sequentially in the instruction-conditioned latent space. By transferring the domain-adapted representations, we improve competitive agents in R2R as measured by the success rate weighted by path length (SPL) metric.
