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What and Who
Title:Learning to propose objects
Speaker:Philipp Krähenbühl
coming from:Stanford University
Speakers Bio:
Event Type:Talk
Visibility:D1, D2, D3, D4, D5, RG1, SWS, MMCI
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Level:MPI Audience
Language:English
Date, Time and Location
Date:Tuesday, 28 April 2015
Time:10:00
Duration:45 Minutes
Location:Saarbrücken
Building:E1 4
Room:633
Abstract
In this talk I’ll present a a new approach for highly accurate bottom-up object segmentation. Given an image, the approach rapidly generates a set of regions that delineate candidate objects in the image. The key idea is to train an ensemble of figure-ground segmentation models directly from a large dataset of annotated object segmentations. Extensive experiments demonstrate that the presented approach outperforms prior object proposal algorithms by a significant margin, while having the lowest running time. The method generalizes well across datasets, indicating that the presented approach is capable of learning a generally applicable model of bottom-up segmentation.
Contact
Name(s):Connie Balzert
Phone:0681 9325 2000
EMail:cbalzert@mpi-inf.mpg.de
Video Broadcast
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Created by:Connie Balzert/MPI-INF, 04/24/2015 09:16 AMLast modified by:Uwe Brahm/MPII/DE, 11/24/2016 04:13 PM
  • Connie Balzert, 04/24/2015 09:16 AM -- Created document.