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Jakob Verbeek (INRIA Grenoble)
Object category localization with incomplete supervision.
On 2015-09-08 11:00 at G205
Object category localization is a challenging problem in computer vision. 
The vast majority of work relies on supervised learning and requires 
bounding box annotations of object instances in the training images. This 
time-consuming annotation process is sidestepped in weakly supervised 
learning. In this case, the supervised information is incomplete and  
restricted to binary labels that indicate the absence/presence of object 
instances in the image, without their locations sizes or aspect ratios. We 
follow a multiple-instance learning approach that iteratively trains the 
detector and infers the object locations in the positive training images. 
Our main contribution is a multi-fold training procedure for multiple 
instance learning, which prevents training from prematurely locking on to 
poor local opima corresponding to erroneous object locations. This 
procedure is particularly important when using high-dimensional 
representations, such as Fisher vectors and convolutional neural network 
features. We also propose a window refinement method, which further 
improves localization accuracy by incorporating a prior on object layout 
based on low-level contour information. We present a detailed experimental 
evaluation using the PASCAL VOC 2007 dataset, which verifies the 
effectiveness of our approach.
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