  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.
