Web Reference: We presented CRF-RNN, an interpretation of dense CRFs as Recurrent Neural Networks. Our formulation fully integrates CRF-based probabilistic graphical mod- elling with emerging deep learning techniques. To this end, we formulate Conditional Random Fields with Gaussian pairwise potentials and mean-field approximate inference as Recurrent Neural Networks. This network, called CRF-RNN, is then plugged in as a part of a CNN to obtain a deep network that has desirable properties of both CNNs and CRFs. Dec 1, 2015 · To optimize the intractable CRF objective, they leverage approximate inference algorithms such as loopy belief propagation (LBP), unrolling several inference steps as recurrent neural networks ...
YouTube Excerpt: Shuai Zheng and Sadeep Jayasumana and Bernardino Romera-Paredes and Vibhav Vineet and Zhizhong Su and Dalong Du ...
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