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Learn to Predict Sets Using Feed-Forward Neural Networks
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Cremers, DanielMilan, AntonReid, IanLeal-Taixe, LauraZhu, TianyuRezatofighi, HamidKaskman, RomanMotlagh, Farbod T.Shi, Javen Qinfeng

This paper addresses the task of set prediction using deep feed-forward neural networks. A set is a collection of elements which is invariant under permutation and the size of a set is not fixed in…

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Photometric Depth Super-Resolution
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Cremers, DanielHaefner, BjoernPeng, SongyouVerma, AlokQueau, Yvain

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Video Object Segmentation Without Temporal Information
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Cremers, DanielGool, Luc VanPont-Tuset, JordiManinis, Kevis-KokitsiCaelles, SergiYuhua ChenLeal-Taixe, Laura

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A Region-Based Gauss-Newton Approach to Real-Time Monocular Multiple Object T…
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Cremers, DanielTjaden, HenningSchwanecke, UlrichSchomer, Elmar

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Direct Sparse Odometry
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Cremers, DanielEngel, JakobKoltun, Vladlen

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Spatially Varying Color Distributions for Interactive Multilabel Segmentation
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Nieuwenhuis, ClaudiaCremers, Daniel


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