  Finding good correspondences is a critical prerequisite in many feature based
tasks. Given a putative correspondence set of an image pair, we propose a
neural network which finds correct correspondences by a binary-class classifier
and estimates relative pose through classified correspondences. First, we
analyze that due to the imbalance in the number of correct and wrong
correspondences, the loss function has a great impact on the classification
results. Thus, we propose a new Guided Loss that can directly use evaluation
criterion (Fn-measure) as guidance to dynamically adjust the objective function
during training. We theoretically prove that the perfect negative correlation
between the Guided Loss and Fn-measure, so that the network is always trained
towards the direction of increasing Fn-measure to maximize it. We then propose
a hybrid attention block to extract feature, which integrates the Bayesian
attentive context normalization (BACN) and channel-wise attention (CA). BACN
can mine the prior information to better exploit global context and CA can
capture complex channel context to enhance the channel awareness of the
network. Finally, based on our Guided Loss and hybrid attention block, a
cascade network is designed to gradually optimize the result for more superior
performance. Experiments have shown that our network achieves the
state-of-the-art performance on benchmark datasets. Our code will be available
in https://github.com/wenbingtao/GLHA.
