AWENet: A Self-Supervised Network for Efficient Interest Point Detection and Description

Abstract

We introduce AWENet(Attention-guided Wavelet Enhancement Network), an efficient self-supervised network for joint interest point detection and description that balances com putational speed with feature accuracy. The network preserves f ine structural details while employing multi-scale attention to enhance the discriminability of descriptors, leading to more precise and reliable interest point correspondences. Evaluations on the HPatches dataset demonstrate that AWENet achieves competitive performance in repeatability, localization accuracy, and matching robustness. Its lightweight design ensures fast processing and low computational cost, making it well-suited for applications where efficiency is critical. Qualitative results show that the network generates dense and accurate correspondences under diverse transformations, including changes in viewpoint and illumination. Overall, AWENet provides a practical and effective solution for learning local features, achieving strong matching performance without relying on heavy computation.