Multi-scale representations deeply learned via convolutional neural networks
have shown tremendous importance for various pixel-level prediction problems.
In this paper we present a novel approach that advances the state of the art on
pixel-level prediction in a fundamental aspect, i.e. structured multi-scale
features learning and fusion. In contrast to previous works directly
considering multi-scale feature maps obtained from the inner layers of a
primary CNN architecture, and simply fusing the features with weighted
averaging or concatenation, we propose a probabilistic graph attention network
structure based on a novel Attention-Gated Conditional Random Fields (AG-CRFs)
model for learning and fusing multi-scale representations in a principled
manner. In order to further improve the learning capacity of the network
structure, we propose to exploit feature dependant conditional kernels within
the deep probabilistic framework. Extensive experiments are conducted on four
publicly available datasets (i.e. BSDS500, NYUD-V2, KITTI, and Pascal-Context)
and on three challenging pixel-wise prediction problems involving both discrete
and continuous labels (i.e. monocular depth estimation, object contour
prediction, and semantic segmentation). Quantitative and qualitative results
demonstrate the effectiveness of the proposed latent AG-CRF model and the
overall probabilistic graph attention network with feature conditional kernels
for structured feature learning and pixel-wise prediction.

本研究提出了一种新的方法，即基于概率图注意力网络结构和依赖特征的条件核，在理论上合理地学习和融合多尺度特征，并用于像素级预测，实验结果表明其有效性。

具有条件核的概率图注意力网络用于逐像素预测

Probabilistic Graph Attention Network with Conditional Kernels for  Pixel-Wise Prediction

Recent works have shown that exploiting multi-scale representations deeply
learned via convolutional neural networks (CNN) is of tremendous importance for
accurate contour detection. This paper presents a novel approach for predicting
contours which advances the state of the art in two fundamental aspects, i.e.
multi-scale feature generation and fusion. Different from previous works
directly consider- ing multi-scale feature maps obtained from the inner layers
of a primary CNN architecture, we introduce a hierarchical deep model which
produces more rich and complementary representations. Furthermore, to refine
and robustly fuse the representations learned at different scales, the novel
Attention-Gated Conditional Random Fields (AG-CRFs) are proposed. The
experiments ran on two publicly available datasets (BSDS500 and NYUDv2)
demonstrate the effectiveness of the latent AG-CRF model and of the overall
hierarchical framework.

本文提出了一种新颖的方法来预测轮廓，通过引入分层深度模型以及使用新型的 AG-CRFs 对不同尺度的特征表示进行精细融合，实现了更加精确的轮廓检测，实验结果表明其在 BSDS500 和 NYUDv2 数据集上都表现出了较好的性能。