In saliency detection, every pixel needs contextual information to make
saliency prediction. Previous models usually incorporate contexts holistically.
However, for each pixel, usually only part of its context region is useful and
contributes to its prediction, while some other part may serve as noises and
distractions. In this paper, we propose a novel pixel-wise contextual attention
network, \ie PiCANet, to learn to selectively attend to informative context
locations at each pixel. Specifically, PiCANet generates an attention map over
the context region of each pixel, where each attention weight corresponds to
the relevance of a context location w.r.t the referred pixel. Then, attentive
contextual features can be constructed via selectively incorporating the
features of useful context locations with the learned attention. We propose
three specific formulations of the PiCANet via embedding the pixel-wise
contextual attention mechanism into the pooling and convolution operations with
attending to global or local contexts. All the three models are fully
differentiable and can be integrated with CNNs with joint training. We
introduce the proposed PiCANets into a U-Net architecture for salient object
detection. Experimental results indicate that the proposed PiCANets can
significantly improve the saliency detection performance. The generated global
and local attention can learn to incorporate global contrast and smoothness,
respectively, which help localize salient objects more accurately and highlight
them more uniformly. Consequently, our saliency model performs favorably
against other state-of-the-art methods. Moreover, we also validate that
PiCANets can also improve semantic segmentation and object detection
performances, which further demonstrates their effectiveness and generalization
ability.

本文提出了一个名为 PiCANet 的新型像素级上下文关注网络，通过对上下文区域的关注权重进行学习，选择性地融合有用的上下文特征，最终应用于显著性检测、语义分割和物体检测等领域，相对于其他同类方法性能有显著的提升。

PiCANet：基于像素级上下文注意力学习的精确显著性检测

PiCANet: Pixel-wise Contextual Attention Learning for Accurate Saliency  Detection

Contexts play an important role in the saliency detection task. However,
given a context region, not all contextual information is helpful for the final
task. In this paper, we propose a novel pixel-wise contextual attention
network, i.e., the PiCANet, to learn to selectively attend to informative
context locations for each pixel. Specifically, for each pixel, it can generate
an attention map in which each attention weight corresponds to the contextual
relevance at each context location. An attended contextual feature can then be
constructed by selectively aggregating the contextual information. We formulate
the proposed PiCANet in both global and local forms to attend to global and
local contexts, respectively. Both models are fully differentiable and can be
embedded into CNNs for joint training. We also incorporate the proposed models
with the U-Net architecture to detect salient objects. Extensive experiments
show that the proposed PiCANets can consistently improve saliency detection
performance. The global and local PiCANets facilitate learning global contrast
and homogeneousness, respectively. As a result, our saliency model can detect
salient objects more accurately and uniformly, thus performing favorably
against the state-of-the-art methods.

本文提出了一种像素级的上下文注意力网络 ——PiCANet，旨在学习选择性地注意每个像素的信息上下文位置，通过全局和局部形式，构建所关注的上下文特征，与 U-Net 结构相结合，可以准确且均匀地检测显著对象，在显著性检测性能方面表现优异。