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

Min Norm Point Algorithm for Higher Order MRF-MAP Inference

no code implementations • CVPR 2016 • Ishant Shanu, Chetan Arora, Parag Singla

Current state of the art inference algorithms for general submodular function takes many hours for problems with clique size 16, and fail to scale beyond.