Regularized Frank-Wolfe for Dense CRFs: Generalizing Mean Field and BeyondDownload PDF

21 May 2021, 20:51 (edited 27 Oct 2021)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: mean field, frank-wolfe, conditional gradient, crfs, mrfs, conditional random fields, markov random fields, map inference, semantic segementation
  • TL;DR: This paper proposes a new class of CRF inference algorithms called Regularized Frank-Wolfe that includes existing algorithms, such as mean field or concave-convex procedure, as special cases.
  • Abstract: We introduce regularized Frank-Wolfe, a general and effective algorithm for inference and learning of dense conditional random fields (CRFs). The algorithm optimizes a nonconvex continuous relaxation of the CRF inference problem using vanilla Frank-Wolfe with approximate updates, which are equivalent to minimizing a regularized energy function. Our proposed method is a generalization of existing algorithms such as mean field or concave-convex procedure. This perspective not only offers a unified analysis of these algorithms, but also allows an easy way of exploring different variants that potentially yield better performance. We illustrate this in our empirical results on standard semantic segmentation datasets, where several instantiations of our regularized Frank-Wolfe outperform mean field inference, both as a standalone component and as an end-to-end trainable layer in a neural network. We also show that dense CRFs, coupled with our new algorithms, produce significant improvements over strong CNN baselines.
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