Human-Machine CRFs for Identifying Bottlenecks in Holistic Scene Understanding
Name
CBMM-Memo-020.pdf
Size
1.89 MB
Format
Adobe PDF
Checksum (MD5)
5e10c03c04a327b2155fd92ab18ffcc0
Author(s)
Mottaghi, Roozbeh
Fidler, Sanja
Yuille, Alan L.
Urtasun, Raquel
Parikh, Devi
Date Issued
June 15, 2014
Publisher
Center for Brains, Minds and Machines (CBMM), arXiv
Citation
arXiv:1406.3906
Series/Report no.
CBMM Memo Series;020
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.
Subjects
Object Recognition
Scene Recognition
Vision
Terms of Use
Attribution-NonCommercial 3.0 United States
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