  In this paper, we are committed to designing a high-performance network for
two-view geometry. We first propose a Guided Loss and theoretically establish
the direct negative correlation between the loss and Fn-measure by dynamically
adjusting the weights of positive and negative classes during training, so that
the network is always trained towards the direction of increasing Fn-measure.
By this way, the network can maintain the advantage of the cross-entropy loss
while maximizing the Fn-measure. 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.
