Attention Guided Invariance Selection for Local Feature Descriptors

To copy with the extreme variations of illumination and rotation in the real world, popular descriptors have captured more invariance recently, but more invariance makes descriptors less informative. So this paper designs a unique attention guided framework (named AISLFD) to select appropriate invariance for local feature descriptors, which boosts the performance of descriptors even in the scenes with extreme changes. Specifically, we first explore an efficient multi-scale feature extraction module that provides our local descriptors with more useful information. Besides, we propose a novel parallel self-attention module to get meta descriptors with the global receptive field, which guides the invariance selection more correctly. Compared with state-of-the-art methods, our method achieves competitive performance through sufficient experiments.