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In this paper, we are committed to designing a high-performance network fortwo-view geometry. We first propose a Guided Loss and theoretically establishthe direct negative correlation between the loss and Fn-measure by dynamicallyadjusting the weights of positive and negative classes during training, so thatthe network is always trained towards the direction of increasing Fn-measure.By this way, the network can maintain the advantage of the cross-entropy losswhile maximizing the Fn-measure. We then propose a hybrid attention block toextract feature, which integrates the bayesian attentive context normalization(BACN) and channel-wise attention (CA). BACN can mine the prior information tobetter exploit global context and CA can capture complex channel context toenhance the channel awareness of the network. Finally, based on our Guided Lossand hybrid attention block, a cascade network is designed to gradually optimizethe result for more superior performance. Experiments have shown that ournetwork achieves the state-of-the-art performance on benchmark datasets.

Cascade Network with Guided Loss and Hybrid Attention for Two-view Geometry