  Recent trends in image understanding have pushed for holistic 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 state-of-the-art 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.
