Mean Field inference is central to statistical physics. It has attracted much
interest in the Computer Vision community to efficiently solve problems
expressible in terms of large Conditional Random Fields. However, since it
models the posterior probability distribution as a product of marginal
probabilities, it may fail to properly account for important dependencies
between variables. We therefore replace the fully factorized distribution of
Mean Field by a weighted mixture of such distributions, that similarly
minimizes the KLDivergence to the true posterior. By introducing two new
ideas, namely, conditioning on groups of variables instead of single
ones and using a parameter of the conditional random field potentials, that
we identify to the temperature in the sense of statistical physics to select
such groups, we can perform this minimization efficiently. Our extension of
the clamping method proposed in previous works allows us to both produce a more
descriptive approximation of the true posterior and, inspired by the diverse
MAP paradigms, fit a mixture of Mean Field approximations. We demonstrate that
this positively impacts realworld algorithms that initially relied on mean
fields.
Pierre Bruno Baque is PhD student at the Computer Vision Lab, EPFL Lausanne.
Currently he his visiting Honeywell, Prague under EU FP7 project CENTAUR.
