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- Title
Weakly Supervised Localization and Learning with Generic Knowledge.
- Authors
Deselaers, Thomas; Alexe, Bogdan; Ferrari, Vittorio
- Abstract
Learning a new object class from cluttered training images is very challenging when the location of object instances is unknown, i.e. in a weakly supervised setting. Many previous works require objects covering a large portion of the images. We present a novel approach that can cope with extensive clutter as well as large scale and appearance variations between object instances. To make this possible we exploit generic knowledge learned beforehand from images of other classes for which location annotation is available. Generic knowledge facilitates learning any new class from weakly supervised images, because it reduces the uncertainty in the location of its object instances. We propose a conditional random field that starts from generic knowledge and then progressively adapts to the new class. Our approach simultaneously localizes object instances while learning an appearance model specific for the class. We demonstrate this on several datasets, including the very challenging Pascal VOC 2007. Furthermore, our method allows training any state-of-the-art object detector in a weakly supervised fashion, although it would normally require object location annotations.
- Subjects
LOCALIZATION (Mathematics); IMAGE processing; ANNOTATIONS; RANDOM fields; VARIATIONS (Aesthetics)
- Publication
International Journal of Computer Vision, 2012, Vol 100, Issue 3, p275
- ISSN
0920-5691
- Publication type
Article
- DOI
10.1007/s11263-012-0538-3