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Multi-scale representations deeply learned via convolutional neural networkshave shown tremendous importance for various pixel-level prediction problems.In this paper we present a novel approach that advances the state of the art onpixel-level prediction in a fundamental aspect, i.e. structured multi-scalefeatures learning and fusion. In contrast to previous works directlyconsidering multi-scale feature maps obtained from the inner layers of aprimary CNN architecture, and simply fusing the features with weightedaveraging or concatenation, we propose a probabilistic graph attention networkstructure based on a novel Attention-Gated Conditional Random Fields (AG-CRFs)model for learning and fusing multi-scale representations in a principledmanner. In order to further improve the learning capacity of the networkstructure, we propose to exploit feature dependant conditional kernels withinthe deep probabilistic framework. Extensive experiments are conducted on fourpublicly available datasets (i.e. BSDS500, NYUD-V2, KITTI, and Pascal-Context)and on three challenging pixel-wise prediction problems involving both discreteand continuous labels (i.e. monocular depth estimation, object contourprediction, and semantic segmentation). Quantitative and qualitative resultsdemonstrate the effectiveness of the proposed latent AG-CRF model and theoverall probabilistic graph attention network with feature conditional kernelsfor structured feature learning and pixel-wise prediction.

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