title: Human-Machine CRFs for Identifying Bottlenecks in Scene Understanding.
abstract: Recent trends in image understanding have pushed for scene understanding models that jointly reason about various tasks such as object detection, scene recognition, shape analysis, contextual reasoning, and local appearance based classifiers. In this work, we are interested in understanding the roles of these different tasks in improved scene understanding, in particular semantic segmentation, object detection and scene recognition. Towards this goal, we "plug-in" human subjects for each of the various components in a conditional random field model. Comparisons among various hybrid human-machine CRFs give us indications of how much "head room" there is to improve scene understanding by focusing research efforts on various individual tasks.
authors.forename: Roozbeh
authors.initials: R
authors.lastname: Mottaghi
authors.forename: Sanja
authors.initials: S
authors.lastname: Fidler
authors.forename: Alan
authors.initials: A
authors.lastname: Yuille
authors.forename: Raquel
authors.initials: R
authors.lastname: Urtasun
authors.forename: Devi
authors.initials: D
authors.lastname: Parikh
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence / Q000706:statistics & numerical data*; D062207:Brain-Computer Interfaces / Q000706:statistics & numerical data*; D003198:Computer Simulation; D016208:Databases, Factual; D006801:Humans; D010363:Pattern Recognition, Automated / Q000706:statistics & numerical data; D010364:Pattern Recognition, Visual