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What and Who
Title:Richer Object Representations for Object Class Detection in Challenging Real-World Images
Speaker:Bojan Pepikj
coming from:Max-Planck-Institut für Informatik - D2
Speakers Bio:
Event Type:Promotionskolloquium
Visibility:D1, D2, D3, D4, D5, RG1, SWS, MMCI
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Level:Public Audience
Language:English
Date, Time and Location
Date:Monday, 21 December 2015
Time:15:00
Duration:60 Minutes
Location:Saarbrücken
Building:E1 4
Room:024
Abstract
Object class detection in real world images has been a synonym for object localization for the longest time.  State-of-the-art detection methods, inspired by renowned detection benchmarks, typically target 2D bounding box localization of objects.  At the same time, due to the rapid technological and scientific advances, high-level vision applications, aiming at understanding the visual world as a whole, are coming into the focus. The diversity of the visual world challenges these applications in terms of representational complexity, robust inference and training data. As objects play a central role in any vision system, it has been argued that richer object representations, providing higher level of detail than modern detection methods, are a promising direction towards understanding visual scenes. Besides bridging the gap between object class detection and high-level tasks, richer object representations also lead to more natural object descriptions, bringing computer vision closer to human perception. Inspired by these prospects, this thesis explores four different directions towards richer object representations, namely, 3D object representations, fine-grained representations, occlusion representations, as well as understanding convnet representations. Moreover, this thesis illustrates that richer object representations can facilitate high-level applications, providing detailed and natural object descriptions. In addition, the presented representations attain high performance rates, at least on par or often superior to state-of-the-art methods.
Contact
Name(s):Connie Balzert
Phone:0681 9325-2000
EMail:cbalzert@mpi-inf.mpg.de
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Created by:Connie Balzert/MPI-INF, 12/14/2015 11:16 AMLast modified by:Uwe Brahm/MPII/DE, 11/24/2016 04:13 PM
  • Connie Balzert, 12/14/2015 11:20 AM
  • Connie Balzert, 12/14/2015 11:16 AM -- Created document.