Higher-order inference in conditional random fields using submodular functions - Pansari, P (2023)
Higher-order and dense conditional random fields (CRFs) are expressive graphical models which have been very successful in low-level computer vision applications such as semantic segmentation, and stereo matching. These models are able to capture long-range interactions and higher-order image statistics much better than pairwise CRFs. This expressive power comes at a price though - inference problems in these models are computationally very demanding. This is a particular challenge in computer vision, where fast inference is important and the problem involves millions of pixels.