Fusion representation learning for keypoint detection and description

Keypoint detection and description are the basis of many computer vision applications such as object recognition and image analysis. Current deep learning-based methods have made great progress in joint learning of keypoint detection and description construction. Low-level features have been proved to be helpful for keypoint detection and description. However, current detector and descriptor focus more on high-level feature and ignore the importance of low-level feature. They simply concatenate features and are lack of sufficient feature fusion. In this work, we propose a fusion representation learning network, which fuses different levels of features for both detectors and descriptors. Furthermore, we design and propose an adaptive feature fusion structure for the descriptor. Extensive experiments on HPatches, FM-Bench and Day-Night datasets demonstrate the superiority of our approach.