English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Many annotation problems in computer vision can be phrased as integer linearprograms (ILPs). The use of standard industrial solvers does not to exploit theunderlying structure of such problems eg, the skeleton in pose estimation. Theleveraging of the underlying structure in conjunction with industrial solverspromises increases in both speed and accuracy. Such structure can be exploitedusing Bender's decomposition, a technique from operations research, that solvescomplex ILPs or mixed integer linear programs by decomposing them intosub-problems that communicate via a master problem. The intuition is thatconditioned on a small subset of the variables the solution to the remainingvariables can be computed easily by taking advantage of properties of the ILPconstraint matrix such as block structure. In this paper we apply Bendersdecomposition to a typical problem in computer vision where we have manysub-ILPs (eg, partitioning of detections, body-parts) coupled to a master ILP(eg, constructing skeletons). Dividing inference problems into a master problemand sub-problems motivates the development of a plethora of novel models, andinference approaches for the field of computer vision.

Exploiting skeletal structure in computer vision annotation with Benders decomposition