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Information × Registration Number 2121U003118, Article popup.category Препринт Title popup.author Viniavskyi Ostap popup.publication 01-01-2021 popup.source_user Український католицький університет popup.source https://hdl.handle.net/20.500.14570/2874 popup.publisher Description In the feature matching problem, local keypoint representations are often not sufficiently distinctive to disambiguate repetitive textures. State-of-the-art matching pipelines encode global information and embed context into keypoint descriptors to resolve this issue. In this thesis, we evaluate the failure modes of the state-ofthe- art method for image matching. We identify the problem that including global context to keypoint representations can sometimes eliminate their distinctiveness. We propose to enhance the learning of the state-of-the-art pipeline by adding a metric learning component to its objective function. By learning more distinctive global context-aware keypoint descriptors, we recover the filtered matches without the loss in matching precision. popup.nrat_date 2025-05-09 Close