JRA-Net: Joint representation attention network for correspondence learning

Ziwei Shi, Guobao Xiao, Linxin Zheng, Jiayi Ma, Riqing Chen

Abstract: Highlights•We design a three layer deep learning framework for outlier rejection.•We propose a novel joint representation attention mechanism.•We design an innovative weight function to improve the generalization ability.•Experimental results show the proposed network is superior to state of the art networks.