提出自监督图网络模型，预测多点触觉感知重要性
Self-supervised Spatio-Temporal Graph Mask-Passing Attention Network for Perceptual Importance Prediction of Multi-point Tactility
- 用自监督时空图神经网络建模多点触觉的感知重要性
- 在多点触觉场景中有效预测各点感知重要性，提升压缩效率
- 适合触觉交互、低延迟触觉压缩系统研发者参考
尽管视觉和听觉信息在现代多媒体系统中广泛应用，触觉与运动觉交互仍提供独特的感知方式。然而，接触式多媒体技术发展较非接触技术滞后，亟需突破。专用触觉媒体技术需低延迟与低比特率，推动触觉信息压缩发展。现有基于感知模型的振动触觉信号压缩方法未考虑多点空间分布下的融合触觉特性。事实上，触觉感知重要性差异不仅存在于传统频率与时域，更体现在皮肤上空间位置的独特性。针对最常用的触觉信息——振动触觉纹理感知，本文构建了一种基于自监督学习与时空图神经网络的感知重要性预测模型。实验结果表明，该模型可有效预测多点触觉感知场景中各点的感知重要性。
原文摘要 · Abstract (English)
While visual and auditory information are prevalent in modern multimedia systems, haptic interaction, e.g., tactile and kinesthetic interaction, provides a unique form of human perception. However, multimedia technology for contact interaction is less mature than non-contact multimedia technologies and requires further development. Specialized haptic media technologies, requiring low latency and bitrates, are essential to enable haptic interaction, necessitating haptic information compression. Existing vibrotactile signal compression methods, based on the perceptual model, do not consider the characteristics of fused tactile perception at multiple spatially distributed interaction points. In fact, differences in tactile perceptual importance are not limited to conventional frequency and time domains, but also encompass differences in the spatial locations on the skin unique to tactile perception. For the most frequently used tactile information, vibrotactile texture perception, we have developed a model to predict its perceptual importance at multiple points, based on self-supervised learning and Spatio-Temporal Graph Neural Network. Current experimental results indicate that this model can effectively predict the perceptual importance of various points in multi-point tactile perception scenarios.
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