资源论文Hyperdynamics Importance Sampling

Hyperdynamics Importance Sampling

2020-03-24 | |  61 |   39 |   0

Abstract

Sequential random sampling (‘Markov Chain Monte-Carlo’) is a popular strategy for many vision problems involving multimodal distributions over high-dimensional parameter spaces. It applies both to importance sampling (where one wants to sample points according to their ‘importance’ for some calculation, but otherwise fairly) and to global optimization (where one wants to find good minima, or at least good starting points for local minimization, regardless of fairness). Unfortunately, most sequential samplers are very prone to becoming ‘trapped’ for long periods in unrepresentative local minima, which leads to biased or highly variable estimates. We present a general strategy for reducing MCMC trapping that generalizes Voter’s ‘hyperdynamic sampling’ from computational chemistry. The local gradient and curvature of the input distribution are used to construct an adaptive importance sampler that focuses samples on low cost negative curvature regions likely to contain ‘transition states’ — codimension-1 saddle points representing ‘mountain passes’ connecting adjacent cost basins. This substantially accelerates inter-basin transition rates while still preserving correct relative transition probabilities. Experimental tests on the difficult problem of 3D articulated human pose estimation from monocular images show signi ficantly enhanced minimum exploration.

上一篇:A Pseudo-Metric for Weighted Point Sets

下一篇:Stratiÿed Self Calibration from Screw-Transform Manifolds

用户评价
全部评价

热门资源

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...