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Dmytro Mishkin
presents
HardNet: a Convolutional Network for Local Image Description
 
On 2017-06-06 14:30 at G205
 
In the talk, a novel loss for learning local feature descriptors inspired by
the
SIFT matching scheme will be presented. We show that the  proposed loss that
relies on the maximization of the distance between the closest positive and
closest negative patches can replace more complex regularization methods which
have been used in local descriptor learning. The loss  works well for both
shallow and deep convolution network architectures. The resulting descriptor is
compact -- it has the same dimensionality as SIFT (128), it shows state-of-art
performance on matching, patch verification and retrieval benchmarks and it is
fast to compute on a GPU.
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