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  • Structured-Output Convolutional Neural Networks for Image Dependent Pairwise Relation

    In this paper, we propose a novel model and its training strategy for the CRF.

    Introduction

    Model

    We denote the input image by \(I\), output by \(Y\), set of parameters by \(\Theta\).

    Conditional Random Fields(CRF).

    \begin{equation} S(I,Y,\Theta)=\sum_{i\in V}{\Phi_{i}(I,Y_{i},\Theta)+\sum_{ij\in E}{\Psi_{ij}(I,Y_{i},Y_{j},\Theta)}}\\ \end{equation}
    \begin{equation} \Psi_{ij}(I,Y_{i},Y_{j},\Theta)=\sum_{m=1}^{M}{w_{m}k_{m}(f(Y_{i}),f(Y_{j}))}\\ \end{equation}
    \begin{equation} Y^{*}=\argmax_{Y}{S(I,Y,\Theta)}\\ \end{equation}

    Training

    Experimental Results