ATKey.Net: Keypoint Detection by Handcrafted and Learned CNN with Attention

Abstract

In image matching, it is essential to obtain more stable and effective feature points. This paper proposes Attention Key.net (ATKey.Net) for the keypoint detection task. Handcrafted and Learned CNN filters are used in a shallow multi-scale architecture with an attention module. Handcrafted filters provide anchor structures for learned filters, which localize, score, and rank repeatable features. Learned CNN filters improve the stability and convergence during backpropagation. Shallow multi-scale architecture has fewer parameters and less computational cost. The attention module gives channel importance. The model is trained on ImageNet and evaluated on the HPatches benchmark. The results show that the repeatability and matching performance is better than the experimental detector.