Titlebook: Computer Vision – ECCV 2020; 16th European Confer Andrea Vedaldi,Horst Bischof,Jan-Michael Frahm Conference proceedings 2020 Springer Natur

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Alan J. Auerbach,Laurence J. Kotlikoff be non-differentiable. The surrogate is learned via a deep embedding where the Euclidean distance between the prediction and the ground truth corresponds to the value of the evaluation metric. The effectiveness of the proposed technique is demonstrated in a post-tuning setup, where a trained model

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Economic Policy Conference Seriesand sporting events. However, existing methods for action assessment are mostly limited to individual actions, especially lacking modeling of the asymmetric relations among agents (e.g., between persons and objects); and this limitation undermines their ability to assess actions containingasymmetric

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John P. De New,Klaus F. Zimmermann3D point cloud counterpart lag far behind. Conventional methods use radius search or other similar methods for aggregating local information. However, these methods are unaware of the instance context and fail to realize the boundary and geometric information of an instance, which are critical to se

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Lessons from Statistical Financem, some image related side information, such as captions and tags, often reveal underlying relationships across images. In this paper, we present an efficient weakly-supervised learning by using a Side Information Network (SINet), which aims to effectively carry out a large scale classification with

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