论文标题
时间场景分割的移位内存网络
Shift-Memory Network for Temporal Scene Segmentation
论文作者
论文摘要
语义细分在理解空间布局方面取得了很高的准确性。对于基于动态场景的实时任务，我们扩展了时间域中的语义分割，以增强运动的空间精度。我们在流入输入上利用了移位模式网络，以确保零延迟输出。对于转移网络下的数据重叠，本文确定了跨网络层的固定时期重复计算。为了避免这种冗余，我们从编码分组基线来得出一个移位内存网络（SMN），以重复网络值而无需精确损失。经过补丁模式训练，SMN提取了SMN的网络参数，以在紧凑的内存中迅速进行推断。我们从1D扫描输入和2D视频中划分动态场景。 SMN的实验具有等效精度为移位模式，但以更快的推理速度和较小的内存。这将有助于在边缘设备上实时应用中的语义细分。
Semantic segmentation has achieved great accuracy in understanding spatial layout. For real-time tasks based on dynamic scenes, we extend semantic segmentation in temporal domain to enhance the spatial accuracy with motion. We utilize a shift-mode network over streaming input to ensure zero-latency output. For the data overlap under shifting network, this paper identifies repeated computation in fixed periods across network layers. To avoid this redundancy, we derive a Shift-Memory Network (SMN) from encoding-decoding baseline to reuse the network values without accuracy loss. Trained in patch-mode, the SMN extracts the network parameters for SMN to perform inference promptly in compact memory. We segment dynamic scenes from 1D scanning input and 2D video. The experiments of SMN achieve equivalent accuracy as shift-mode but in faster inference speeds and much smaller memory. This will facilitate semantic segmentation in real-time application on edge devices.
