Unsupervised learning of object landmarks by factorized spatial embeddings
2017 IEEE International Conference on Computer Vision (ICCV)(2017)
摘要
Learning automatically the structure of object categories remains an important open problem in computer vision. In this paper, we propose a novel unsupervised approach that can discover and learn landmarks in object categories, thus characterizing their structure. Our approach is based on factorizing image deformations, as induced by a viewpoint change or an object deformation, by learning a deep neural network that detects landmarks consistently with such visual effects. Furthermore, we show that the learned landmarks establish meaningful correspondences between different object instances in a category without having to impose this requirement explicitly. We assess the method qualitatively on a variety of object types, natural and man-made. We also show that our unsupervised landmarks are highly predictive of manually-annotated landmarks in face benchmark datasets, and can be used to regress these with a high degree of accuracy.
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关键词
manually-annotated landmarks,unsupervised learning,object landmarks,factorized spatial embeddings,object categories,computer vision,unsupervised approach,image deformations,viewpoint change,object deformation,deep neural network,unsupervised landmarks,landmark detection
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