Anatoly Zelenin

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Deep models, such as Convolutional Neural Networks (CNNs), are omnipresent in computer vision, as well as structured models, such as Conditional Random Fields (CRFs). Combining them brings many advantages, foremost the ability to incorporate prior knowledge into CNNs, e.g. by explicitly modelling the dependencies between output variables. In this work, we(More)
Deep Models, such as Convolutional Neural Networks (CNNs), are omnipresent in computer vision, as well as, structured models, such as Conditional Random Fields (CRFs). Combining them brings many advantages, foremost the ability to explicitly model the dependencies between output variables (CRFs) using thereby the incredible power of CNNs. In this work we(More)
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