We present FasterSeg, an automatically designed semantic segmentation network
with not only state-of-the-art performance but also faster speed than current
methods. Utilizing neural architecture search (NAS), FasterSeg is discovered
from a novel and broader search space integrating multi-resolution branches,
that has been recently found to be vital in manually designed segmentation
models. To better calibrate the balance between the goals of high accuracy and
low latency, we propose a decoupled and fine-grained latency regularization,
that effectively overcomes our observed phenomenons that the searched networks
are prone to "collapsing" to low-latency yet poor-accuracy models. Moreover, we
seamlessly extend FasterSeg to a new collaborative search (co-searching)
framework, simultaneously searching for a teacher and a student network in the
same single run. The teacher-student distillation further boosts the student
model's accuracy. Experiments on popular segmentation benchmarks demonstrate
the competency of FasterSeg. For example, FasterSeg can run over 30% faster
than the closest manually designed competitor on Cityscapes, while maintaining
comparable accuracy.

本文提出 FasterSeg 模型，通过神经架构搜索技术在新型多分辨率支路搜索空间上发现了性能卓越、速度更快的语义分割网络，并提出了一种解决低准确性、低延迟困境的方法，同时实现了教师 - 学生蒸馏机制，提高了学生网络的准确性。实验证明了 FasterSeg 的竞争力。

FasterSeg：搜索更快的实时语义分割

FasterSeg: Searching for Faster Real-time Semantic Segmentation

We focus on the challenging task of real-time semantic segmentation in this
paper. It finds many practical applications and yet is with fundamental
difficulty of reducing a large portion of computation for pixel-wise label
inference. We propose an image cascade network (ICNet) that incorporates
multi-resolution branches under proper label guidance to address this
challenge. We provide in-depth analysis of our framework and introduce the
cascade feature fusion unit to quickly achieve high-quality segmentation. Our
system yields real-time inference on a single GPU card with decent quality
results evaluated on challenging datasets like Cityscapes, CamVid and
COCO-Stuff.

本文提出了一种基于图像级联网络 (ICNet) 的实时语义分割方法，通过多分辨率分支和级联特征融合单元，以及恰当的标签引导来降低像素级标签推断的计算量，从而实现对挑战性数据集（如 Cityscapes，CamVid 和 COCO-Stuff）的高质量实时分割。