---
res:
bibo_abstract:
- "Structural support vector machines (SSVMs) are amongst the best performing models
for structured computer vision tasks, such as semantic image segmentation or human
pose estimation. Training SSVMs, however, is computationally costly, because it
requires repeated calls to a structured prediction subroutine (called \\emph{max-oracle}),
which has to solve an optimization problem itself, e.g. a graph cut.\r\nIn this
work, we introduce a new algorithm for SSVM training that is more efficient than
earlier techniques when the max-oracle is computationally expensive, as it is
frequently the case in computer vision tasks. The main idea is to (i) combine
the recent stochastic Block-Coordinate Frank-Wolfe algorithm with efficient hyperplane
caching, and (ii) use an automatic selection rule for deciding whether to call
the exact max-oracle or to rely on an approximate one based on the cached hyperplanes.\r\nWe
show experimentally that this strategy leads to faster convergence to the optimum
with respect to the number of requires oracle calls, and that this translates
into faster convergence with respect to the total runtime when the max-oracle
is slow compared to the other steps of the algorithm. @eng"
bibo_authorlist:
- foaf_Person:
foaf_givenName: Neel
foaf_name: Shah, Neel
foaf_surname: Shah
foaf_workInfoHomepage: http://www.librecat.org/personId=31ABAF80-F248-11E8-B48F-1D18A9856A87
- foaf_Person:
foaf_givenName: Vladimir
foaf_name: Kolmogorov, Vladimir
foaf_surname: Kolmogorov
foaf_workInfoHomepage: http://www.librecat.org/personId=3D50B0BA-F248-11E8-B48F-1D18A9856A87
- foaf_Person:
foaf_givenName: Christoph
foaf_name: Lampert, Christoph
foaf_surname: Lampert
foaf_workInfoHomepage: http://www.librecat.org/personId=40C20FD2-F248-11E8-B48F-1D18A9856A87
orcid: 0000-0001-8622-7887
bibo_doi: 10.1109/CVPR.2015.7298890
dct_date: 2015^xs_gYear
dct_language: eng
dct_publisher: IEEE@
dct_title: A multi-plane block-coordinate Frank-Wolfe algorithm for training structural
SVMs with a costly max-oracle@
...