Panoptic SwiftNet：用于实时全景分割的金字塔融合
Panoptic SwiftNet: Pyramidal Fusion for Real-time Panoptic Segmentation
AI总结：
本文提出 Panoptic SwiftNet，通过尺度等变特征提取、金字塔融合和边界感知学习，在遥感等场景中实现实时全景分割，并在 BSB-Aerial 上超越现有方法。
摘要：
稠密全景预测是自动驾驶、自动化仓储或遥感等许多现有应用中的关键组成部分。这些应用中有许多需要在可负担甚至嵌入式硬件上对高输入分辨率进行快速推理。我们提出通过以骨干网络容量换取多尺度特征提取来实现这一目标。与同时期的全景分割方法相比，我们方法的主要创新之处在于高效的尺度等变特征提取、通过金字塔融合实现的跨尺度上采样以及像素到实例分配的边界感知学习。由于典型城市级和区域级数据集中包含海量像素，所提出的方法非常适合遥感影像。我们在 Cityscapes、Vistas、COCO 和 BSB-Aerial 数据集上进行了全景分割实验。我们的模型在 BSB-Aerial 数据集上超越了当前最先进水平，同时能够在 RTX3090 GPU 上以 FP16 精度和 TensorRT 优化每秒处理超过一百张 1MPx 图像。
英文摘要：
Dense panoptic prediction is a key ingredient in many existing applications such as autonomous driving, automated warehouses or remote sensing. Many of these applications require fast inference over large input resolutions on affordable or even embedded hardware. We propose to achieve this goal by trading off backbone capacity for multi-scale feature extraction. In comparison with contemporaneous approaches to panoptic segmentation, the main novelties of our method are efficient scale-equivariant feature extraction, cross-scale upsampling through pyramidal fusion and boundary-aware learning of pixel-to-instance assignment. The proposed method is very well suited for remote sensing imagery due to the huge number of pixels in typical city-wide and region-wide datasets. We present panoptic experiments on Cityscapes, Vistas, COCO and the BSB-Aerial dataset. Our models outperform the state of the art on the BSB-Aerial dataset while being able to process more than a hundred 1MPx images per second on a RTX3090 GPU with FP16 precision and TensorRT optimization.
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。
