用于多点触觉感知重要性预测的自监督时空图掩码传递注意力网络
Self-supervised Spatio-Temporal Graph Mask-Passing Attention Network for Perceptual Importance Prediction of Multi-point Tactility
AI总结：
针对多点触觉感知重要性预测问题，提出基于自监督学习和时空图神经网络的模型，有效预测多点触觉场景中各点的感知重要性。
摘要：
尽管视觉和听觉信息在现代多媒体系统中很普遍，但触觉交互（例如触觉和动觉交互）提供了一种独特的人类感知形式。然而，用于接触式交互的多媒体技术不如非接触式多媒体技术成熟，需要进一步发展。专用的触觉媒体技术需要低延迟和比特率，对于实现触觉交互至关重要，因此需要触觉信息压缩。现有的基于感知模型的振动触觉信号压缩方法没有考虑在多个空间分布交互点处的融合触觉感知特性。事实上，触觉感知重要性的差异不仅限于传统的频率和时间域，还包括触觉感知特有的皮肤空间位置差异。对于最常用的触觉信息——振动触觉纹理感知，我们开发了一个模型来预测其在多个点的感知重要性，该模型基于自监督学习和时空图神经网络。目前的实验结果表明，该模型能够有效预测多点触觉感知场景中各个点的感知重要性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
