An Inference Algorithm for Multi-Label MRF-MAP Problems with Clique Size 100

1 code implementation • ECCV 2020 • Ishant Shanu, Siddhant Bharti, Chetan Arora, S. N. Maheshwari

Earlier algorithms based on this transformation could not handle problems larger than 16 labels on cliques of size 4.

Semantic Segmentation Stereo Matching

Inference in Higher Order MRF-MAP Problems With Small and Large Cliques

no code implementations • CVPR 2018 • Ishant Shanu, Chetan Arora, S. N. Maheshwari

Two popular combinatorial approaches for solving such formulations are flow based and polyhedral approaches.

Semantic Segmentation

Fast Approximate Inference in Higher Order MRF-MAP Labeling Problems

no code implementations • CVPR 2014 • Chetan Arora, Subhashis Banerjee, Prem Kalra, S. N. Maheshwari

Generic Cuts (GC) of Arora et al. [9] shows that when potentials are submodular, inference problems can be solved optimally in polynomial time for fixed size cliques.

Multi Label Generic Cuts: Optimal Inference in Multi Label Multi Clique MRF-MAP Problems

no code implementations • CVPR 2014 • Chetan Arora, S. N. Maheshwari

We exploit sparseness in the feasible configurations of the transformed 2-label problem to suggest an improvement to Generic Cuts [3] to solve the 2-label problems efficiently.