Human-Machine CRFs for Identifying Bottlenecks in Holistic Scene Understanding.

TitleHuman-Machine CRFs for Identifying Bottlenecks in Holistic Scene Understanding.
Publication TypeCBMM Memos
Year of Publication2014
AuthorsMottaghi, R, Fidler, S, Yuille, A, Urtasun, R, Parikh, D
Number020
Date Published06/2014
Abstract

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.

arXiv

arXiv:1406.3906

DSpace@MIT

http://hdl.handle.net/1721.1/100184

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