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  1. Vedaldi A., Bischof H. (Ed.)
  2. Abstract

    The angular dependence of the microwave-driven spin rectification (SR) effect in single crystalline Co0.5Fe0.5alloy film is systematically investigated. Due to the strong current-orientation dependent anisotropic magnetoresistance (AMR), the SR effects in CoFe film strongly deviate from the ordinary sin 2φMcosφMrelation withφMdefined as the magnetization angle away from the current. A giant Gilbert damping anisotropy in the CoFe film with a maximum–minimum ratio of 520% is observed, which can impose a strong anisotropy onto magnetic susceptibility. The observed unusual angular dependence can be well explained by the theory including current-orientation dependent AMR and anisotropic magnetic susceptibility. Our work also suggests that the strong current-orientation dependent AMR in single crystalline CoFe film could exist up to the gigahertz frequency range.

  3. Our goal in this paper is to discover near duplicate patterns in large collections of artworks. This is harder than standard instance mining due to differences in the artistic media (oil, pastel, drawing, etc), and imperfections inherent in the copying process. The key technical insight is to adapt a standard deep feature to this task by fine-tuning it on the specific art collection using self-supervised learning. More specifically, spatial consistency between neighbouring feature matches is used as supervisory fine-tuning signal. The adapted feature leads to more accurate style-invariant matching, and can be used with a standard discovery approach, based on geometric verification, to identify duplicate patterns in the dataset. The approach is evaluated on several different datasets and shows surprisingly good qualitative discovery results. For quantitative evaluation of the method, we annotated 273 near duplicate details in a dataset of 1587 artworks attributed to Jan Brueghel and his workshop. Beyond artwork, we also demonstrate improvement on localization on the Oxford5K photo dataset as well as on historical photograph localization on the Large Time Lags Location (LTLL) dataset.