触覚の言語化：二重分岐学習による振動からテキストへの変換
The Language of Touch: Translating Vibrations into Text with Dual-Branch Learning
振動触覚信号から自然言語の説明文を生成する初の試みとして、周期・非周期成分を分離する二重分岐学習と動的融合機構を備えたViPACを提案し、専用データセットLMT108-CAPを構築して有効性を実証した。
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著者: Jin Chen, Yifeng Lin, Chao Zeng, Si Wu, Tiesong Zhao
分類: cs.CV, cs.AI
原文アブストラクト
The standardization of vibrotactile data by IEEE P1918.1 workgroup has greatly advanced its applications in virtual reality, human-computer interaction and embodied artificial intelligence. Despite these efforts, the semantic interpretation and understanding of vibrotactile signals remain an unresolved challenge. In this paper, we make the first attempt to address vibrotactile captioning, {\it i.e.}, generating natural language descriptions from vibrotactile signals. We propose Vibrotactile Periodic-Aperiodic Captioning (ViPAC), a method designed to handle the intrinsic properties of vibrotactile data, including hybrid periodic-aperiodic structures and the lack of spatial semantics. Specifically, ViPAC employs a dual-branch strategy to disentangle periodic and aperiodic components, combined with a dynamic fusion mechanism that adaptively integrates signal features. It also introduces an orthogonality constraint and weighting regularization to ensure feature complementarity and fusion consistency. Additionally, we construct LMT108-CAP, the first vibrotactile-text paired dataset, using GPT-4o to generate five constrained captions per surface image from the popular LMT-108 dataset. Experiments show that ViPAC significantly outperforms the baseline methods adapted from audio and image captioning, achieving superior lexical fidelity and semantic alignment.
