泛视Lintention网络：为视障者提供有效的导航感知
Panoptic Lintention Network: Towards Efficient Navigational Perception for the Visually Impaired
摘要：
经典的计算机视觉算法、实例分割和语义分割不能为视觉障碍者提供对周围环境的整体理解。在本文中，我们利用全景分割，以协助视障人士的导航，有效地提供在视障人士附近的东西和东西的意识。为此，我们提出了一个有效的Attention模块--Linintention，它可以用线性空间来模拟线性时间内的长程相互作用。在此基础上，我们提出了一种新的全景分割模型，我们称之为全景分割网。在COCO数据集上的实验表明，Panoptic Lininty Net将Panoptic Quality（PQ）从39.39提高到41.42，性能提高4.6%，而仅需要减少10%的GFLOPs和25%的语义分支参数。此外，通过我们设计的紧凑型可穿戴全景分割系统的真实世界的测试，表明我们的系统的基础上的Panoptic Lintention Net完成了一个相对稳定的和非常显着的全景分割在现实世界的场景。
英文摘要：
Classic computer vision algorithms, instance segmentation, and semantic segmentation can not provide a holistic understanding of the surroundings for the visually impaired. In this paper, we utilize panoptic segmentation to assist the navigation of visually impaired people by offering both things and stuff awareness in the proximity of the visually impaired efficiently. To this end, we propose an efficient Attention module -- Lintention which can model long-range interactions in linear time using linear space. Based on Lintention, we then devise a novel panoptic segmentation model which we term Panoptic Lintention Net. Experiments on the COCO dataset indicate that the Panoptic Lintention Net raises the Panoptic Quality (PQ) from 39.39 to 41.42 with 4.6\% performance gain while only requiring 10\% fewer GFLOPs and 25\% fewer parameters in the semantic branch. Furthermore, a real-world test via our designed compact wearable panoptic segmentation system, indicates that our system based on the Panoptic Lintention Net accomplishes a relatively stable and exceptionally remarkable panoptic segmentation in real-world scenes.
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。
