In this paper, we propose quantized densely connected U-Nets for efficient
visual landmark localization. The idea is that features of the same semantic
meanings are globally reused across the stacked U-Nets. This dense connectivity
largely improves the information flow, yielding improved localization accuracy.
However, a vanilla dense design would suffer from critical efficiency issue in
both training and testing. To solve this problem, we first propose order-K
dense connectivity to trim off long-distance shortcuts; then, we use a
memory-efficient implementation to significantly boost the training efficiency
and investigate an iterative refinement that may slice the model size in half.
Finally, to reduce the memory consumption and high precision operations both in
training and testing, we further quantize weights, inputs, and gradients of our
localization network to low bit-width numbers. We validate our approach in two
tasks: human pose estimation and face alignment. The results show that our
approach achieves state-of-the-art localization accuracy, but using ~70% fewer
parameters, ~98% less model size and saving ~75% training memory compared with
other benchmark localizers. The code is available at
this https URL

本文提出了针对视觉地标定位的量化密集连接 U-Nets，通过采用 order-K 密集连接，并使用内存高效的实现来提高训练效率，以及使用低位宽数字量化权重、输入和梯度来降低内存消耗和高精度操作，实现了比其他基准定位器使用约 70% 更少参数、98% 更小的模型尺寸以及节省约 75% 训练内存的优异定位精度。