To achieve parsimonious inference in per-pixel labeling tasks with a limited
computational budget, we propose a \emph{Pixel-wise Attentional Gating} unit
(\emph{PAG}) that learns to selectively process a subset of spatial locations
at each layer of a deep convolutional network. PAG is a generic,
architecture-independent, problem-agnostic mechanism that can be readily
"plugged in" to an existing model with fine-tuning. We utilize PAG in two ways:
1) learning spatially varying pooling fields that improve model performance
without the extra computation cost associated with multi-scale pooling, and 2)
learning a dynamic computation policy for each pixel to decrease total
computation while maintaining accuracy.
We extensively evaluate PAG on a variety of per-pixel labeling tasks,
including semantic segmentation, boundary detection, monocular depth and
surface normal estimation. We demonstrate that PAG allows competitive or
state-of-the-art performance on these tasks. Our experiments show that PAG
learns dynamic spatial allocation of computation over the input image which
provides better performance trade-offs compared to related approaches (e.g.,
truncating deep models or dynamically skipping whole layers). Generally, we
observe PAG can reduce computation by $10\%$ without noticeable loss in
accuracy and performance degrades gracefully when imposing stronger
computational constraints.

本篇论文提出一种基于 Pixel-wise Attentional Gating 的模型优化方案，使得在计算资源受限的前提下，模型在特定任务上（如：语义分割、边界检测、深度估计等）可以达到与其他相关方法相抗衡或处于领先地位。这种方案可以通过学习空间变化的池化区域来提高模型性能，同时针对每个像素学习动态计算策略，以降低总体计算量，并在不影响准确性的情况下提供更好的性能平衡。