论文标题
Hyperseg：针对实时语义细分的贴片超级net工作
HyperSeg: Patch-wise Hypernetwork for Real-time Semantic Segmentation
论文作者
论文摘要
我们提出了一个新颖的，实时的语义分割网络，其中编码器都编码并生成解码器的参数（权重）。此外，为了允许最大的适应性，每个解码器块上的权重变化。为此，我们设计了一种新型的超网络，由嵌套的U-NET组成，用于绘制更高级别的上下文功能，该功能是一个多头重量生成模块，该模块在消耗之前会在解码器中产生每个块的重量，以进行有效的存储器利用，以及由小型的小型网络组成的主要网络。尽管使用较少规定的块，但我们的架构仍获得实时性能。就运行时与准确性权衡方面而言，我们超过了流行的语义细分基准的结果：Pascal VOC 2012（Val。Set）（val。Set）和有关CityScapes的实时语义细分和Camvid。该代码可用：https：//nirkin.com/hyperseg。
We present a novel, real-time, semantic segmentation network in which the encoder both encodes and generates the parameters (weights) of the decoder. Furthermore, to allow maximal adaptivity, the weights at each decoder block vary spatially. For this purpose, we design a new type of hypernetwork, composed of a nested U-Net for drawing higher level context features, a multi-headed weight generating module which generates the weights of each block in the decoder immediately before they are consumed, for efficient memory utilization, and a primary network that is composed of novel dynamic patch-wise convolutions. Despite the usage of less-conventional blocks, our architecture obtains real-time performance. In terms of the runtime vs. accuracy trade-off, we surpass state of the art (SotA) results on popular semantic segmentation benchmarks: PASCAL VOC 2012 (val. set) and real-time semantic segmentation on Cityscapes, and CamVid. The code is available: https://nirkin.com/hyperseg.
