Matches in SemOpenAlex for { <https://semopenalex.org/work/W4297492734> ?p ?o ?g. }
Showing items 1 to 68 of
68
with 100 items per page.
- W4297492734 abstract "Dense conditional random fields (CRFs) have become a popular framework for modelling several problems in computer vision such as stereo correspondence and multi-class semantic segmentation. By modelling long-range interactions, dense CRFs provide a labelling that captures finer detail than their sparse counterparts. Currently, the state-of-the-art algorithm performs mean-field inference using a filter-based method but fails to provide a strong theoretical guarantee on the quality of the solution. A question naturally arises as to whether it is possible to obtain a maximum a posteriori (MAP) estimate of a dense CRF using a principled method. Within this paper, we show that this is indeed possible. We will show that, by using a filter-based method, continuous relaxations of the MAP problem can be optimised efficiently using state-of-the-art algorithms. Specifically, we will solve a quadratic programming (QP) relaxation using the Frank-Wolfe algorithm and a linear programming (LP) relaxation by developing a proximal minimisation framework. By exploiting labelling consistency in the higher-order potentials and utilising the filter-based method, we are able to formulate the above algorithms such that each iteration has a complexity linear in the number of classes and random variables. The presented algorithms can be applied to any labelling problem using a dense CRF with sparse higher-order potentials. In this paper, we use semantic segmentation as an example application as it demonstrates the ability of the algorithm to scale to dense CRFs with large dimensions. We perform experiments on the Pascal dataset to indicate that the presented algorithms are able to attain lower energies than the mean-field inference method." @default.
- W4297492734 created "2022-09-29" @default.
- W4297492734 creator A5001534492 @default.
- W4297492734 creator A5004223779 @default.
- W4297492734 creator A5013834379 @default.
- W4297492734 creator A5027893749 @default.
- W4297492734 creator A5039055995 @default.
- W4297492734 creator A5049300388 @default.
- W4297492734 creator A5057981746 @default.
- W4297492734 creator A5078217575 @default.
- W4297492734 date "2018-05-23" @default.
- W4297492734 modified "2023-10-16" @default.
- W4297492734 title "Efficient Relaxations for Dense CRFs with Sparse Higher Order Potentials" @default.
- W4297492734 doi "https://doi.org/10.48550/arxiv.1805.09028" @default.
- W4297492734 hasPublicationYear "2018" @default.
- W4297492734 type Work @default.
- W4297492734 citedByCount "0" @default.
- W4297492734 crossrefType "posted-content" @default.
- W4297492734 hasAuthorship W4297492734A5001534492 @default.
- W4297492734 hasAuthorship W4297492734A5004223779 @default.
- W4297492734 hasAuthorship W4297492734A5013834379 @default.
- W4297492734 hasAuthorship W4297492734A5027893749 @default.
- W4297492734 hasAuthorship W4297492734A5039055995 @default.
- W4297492734 hasAuthorship W4297492734A5049300388 @default.
- W4297492734 hasAuthorship W4297492734A5057981746 @default.
- W4297492734 hasAuthorship W4297492734A5078217575 @default.
- W4297492734 hasBestOaLocation W42974927341 @default.
- W4297492734 hasConcept C106131492 @default.
- W4297492734 hasConcept C11413529 @default.
- W4297492734 hasConcept C121332964 @default.
- W4297492734 hasConcept C152565575 @default.
- W4297492734 hasConcept C154945302 @default.
- W4297492734 hasConcept C163716315 @default.
- W4297492734 hasConcept C2775953691 @default.
- W4297492734 hasConcept C2776214188 @default.
- W4297492734 hasConcept C31972630 @default.
- W4297492734 hasConcept C41008148 @default.
- W4297492734 hasConcept C62520636 @default.
- W4297492734 hasConcept C89600930 @default.
- W4297492734 hasConceptScore W4297492734C106131492 @default.
- W4297492734 hasConceptScore W4297492734C11413529 @default.
- W4297492734 hasConceptScore W4297492734C121332964 @default.
- W4297492734 hasConceptScore W4297492734C152565575 @default.
- W4297492734 hasConceptScore W4297492734C154945302 @default.
- W4297492734 hasConceptScore W4297492734C163716315 @default.
- W4297492734 hasConceptScore W4297492734C2775953691 @default.
- W4297492734 hasConceptScore W4297492734C2776214188 @default.
- W4297492734 hasConceptScore W4297492734C31972630 @default.
- W4297492734 hasConceptScore W4297492734C41008148 @default.
- W4297492734 hasConceptScore W4297492734C62520636 @default.
- W4297492734 hasConceptScore W4297492734C89600930 @default.
- W4297492734 hasLocation W42974927341 @default.
- W4297492734 hasLocation W42974927342 @default.
- W4297492734 hasOpenAccess W4297492734 @default.
- W4297492734 hasPrimaryLocation W42974927341 @default.
- W4297492734 hasRelatedWork W1526362251 @default.
- W4297492734 hasRelatedWork W2129232371 @default.
- W4297492734 hasRelatedWork W2754165133 @default.
- W4297492734 hasRelatedWork W2794772564 @default.
- W4297492734 hasRelatedWork W2800507189 @default.
- W4297492734 hasRelatedWork W2963687373 @default.
- W4297492734 hasRelatedWork W2966354721 @default.
- W4297492734 hasRelatedWork W3019131160 @default.
- W4297492734 hasRelatedWork W4239830733 @default.
- W4297492734 hasRelatedWork W4295602020 @default.
- W4297492734 isParatext "false" @default.
- W4297492734 isRetracted "false" @default.
- W4297492734 workType "article" @default.