Abstract. Feature Matching between images is an essential task for many computer vision and photogrammetry applications, such as Structure from Motion (SFM), Surface Extraction, Visual Simultaneous Localization Mapping (VSLAM), vision-based localization navigation. Among the matched point pairs, there are typically false positive matches. Therefore, outlier detection rejection important steps i...

Heavy computation is a bottleneck limiting deep-learning-based feature matching algorithms to be applied in many real-time applications. However, existing lightweight networks optimized for Euclidean data cannot address classical tasks, since sparse keypoint based descriptors are expected matched. This paper tackles this problem and proposes two concepts: 1) novel parallel attention model entit...

The datasets in the latest semantic segmentation model often need to be manually labeled for each pixel, which is time-consuming and requires much effort. General models are unable make better predictions, new categories of information that have never been seen before, than few-shot has emerged. However, still faced up with two challenges. One inadequate exploration conveyed high-level features...