论文标题
Segblocks：基于块的动态分辨率网络，用于实时分割
SegBlocks: Block-Based Dynamic Resolution Networks for Real-Time Segmentation
论文作者
论文摘要
Segblocks通过根据图像区域的复杂性动态调整处理分辨率来降低现有神经网络的计算成本。我们的方法将图像拆分为低复杂性的块和尺寸块块，从而减少了操作数量和内存消耗的数量。选择复杂区域的轻量级政策网络是使用加强学习训练的。此外，我们介绍了在CUDA中实现的几个模块以处理块中的图像。最重要的是，我们的新颖的阻止模块可以防止现有方法遭受的块边界的特征不连续性，同时保持记忆消耗受到控制。我们对语义分割的城市景观，Camvid和Mapillary Vistas数据集的实验表明，与具有类似复杂性的静态基线相比，动态处理图像提供了更好的精度与复杂性权衡。例如，我们的方法将SwiftNet-RN18的浮点操作数量减少了60％，并将推理速度提高了50％，而CityScapes的MIOU准确性仅降低0.3％。
SegBlocks reduces the computational cost of existing neural networks, by dynamically adjusting the processing resolution of image regions based on their complexity. Our method splits an image into blocks and downsamples blocks of low complexity, reducing the number of operations and memory consumption. A lightweight policy network, selecting the complex regions, is trained using reinforcement learning. In addition, we introduce several modules implemented in CUDA to process images in blocks. Most important, our novel BlockPad module prevents the feature discontinuities at block borders of which existing methods suffer, while keeping memory consumption under control. Our experiments on Cityscapes, Camvid and Mapillary Vistas datasets for semantic segmentation show that dynamically processing images offers a better accuracy versus complexity trade-off compared to static baselines of similar complexity. For instance, our method reduces the number of floating-point operations of SwiftNet-RN18 by 60% and increases the inference speed by 50%, with only 0.3% decrease in mIoU accuracy on Cityscapes.
