In this work we introduce the stochastic segmentation tree (SST) model, which treats multiple ground truths as samples from a target distribution and learns to ...

We develop a new class of hierarchical stochastic image models called spatial random trees (SRTs) which admit polynomial-complexity exact inference ...

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We propose the LAtent VAriable Stochastic Ensemble of Trees (LAVASET) method that derives latent variables based on the distance characteristics of each ...

The BSDS300 consists of 200 training and 100 test images, each with multiple ground-truth segmentations. The BSDS500 uses the BSDS300 as training and adds 200 ...

Stochastic Segmentation Trees for Multiple Ground Truths.‏. J Snell, RS Zemel‏. UAI, 2017‏. 3, 2017. Implicit maximum a posteriori filtering via adaptive ...

Oct 6, 2023 · A stochastically determined threshold will be used to include or exclude an instance to update the other network, based on ground-truth-label ...

We present a stochastic clustering algorithm which uses pairwise similarity of elements, based on a new graph theoretical algorithm for the sampling of cuts ...

Oct 31, 2022 · We structure the uncertainty in stochastic segmentation networks into meaningful components, which we use for fine-grained sampling and adjustment of predicted ...

A tree-structured probabilistic model, the stochastic segmentation tree, that represents a distribution over segmentations of a given image, and is able to ...