Image registration is a technique of matching and superimposing of features, which can be better applied to high-level vision tasks. However, high-level vision tasks often pay more attention to analysis of target areas. In summary, we rethink the collaborative relationship between image registration and high-level vision tasks, and propose a local registration strategy for constructing a robust adaptive attention gauge fields, and construct robust adaptive attention gauge fields to register images focus more on important target areas. In order to improve the robustness and timeliness of the algorithm for feature matching of target areas in complex environments, we propose a robust adaptive probability distribution(RA), and construct robust adaptive mixed model expectation maximum attention(RAMM-EMA). In order to make robust adaptive parameters to reach the global optimum in deep learning, we designed a simulated annealing method to optimize the robust adaptive parameters during training. In order to focus on important regions in the image as much as possible, propose a feature fusion map with weak boundaries and probabilistic properties, called Robust Adaptive Attention Gauge Fields(RAA-Gauge Fields). High-level vision tasks experiments on image fusion, object delect and 3D-reconstruction, the method presented in this paper is the most helpful, and registration effect of the target areas perform best under more extreme conditions.