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Objects as Context for Detecting Their Semantic Parts

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationThe IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Pages6907-6916
Number of pages10
DOIs
Publication statusPublished - 17 Dec 2018
EventComputer Vision and Pattern Recognition 2018 - Salt Lake City, United States
Duration: 18 Jun 201822 Jun 2018
http://cvpr2018.thecvf.com/
http://cvpr2018.thecvf.com/
http://cvpr2018.thecvf.com/

Conference

ConferenceComputer Vision and Pattern Recognition 2018
Abbreviated titleCVPR 2018
CountryUnited States
CitySalt Lake City
Period18/06/1822/06/18
Internet address

Abstract

We present a semantic part detection approach that effectively leverages object information. We use the object appearance and its class as indicators of what parts to expect. We also model the expected relative location of parts inside the objects based on their appearance. We achieve this with a new network module, called OffsetNet, that efficiently predicts a variable number of part locations within a given object. Our model incorporates all these cues to detect parts in the context of their objects. This leads to considerably higher performance for the challenging task of part detection compared to using part appearance alone
(+5 mAP on the PASCAL-Part dataset). We also compare to other part detection methods on both PASCAL-Part and CUB200-2011 datasets.

Event

Computer Vision and Pattern Recognition 2018

18/06/1822/06/18

Salt Lake City, United States

Event: Conference

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