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

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Sharon E. Nicholson,Xungang Ying data to guide the predictions. We evaluate the proposed method on two challenging benchmark datasets (Human3.6M and CMU-Mocap). Experimental results show our superior performance compared with the state-of-the-art approaches.

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Donald D. Adams,Samuel O. Ochola neural network for a classification task, enforcing a consistent labelling amongst samples within a class. We show state-of-the-art results on clustering and image retrieval on several datasets, and show the potential of our method when combined with other techniques such as ensembles. To facilitat

只看該作者 · 發(fā)表于 2025-4-1 19:48:17

Advances in Global Change Researcht is both more efficient, in the sense that it does not require expensive offline training when entering a new domain, and more adaptive as it adapts to the learner state. Our augmentation networks require less domain knowledge and are easily applicable to new tasks. Extensive experiments demonstrat

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只看該作者 · 發(fā)表于 2025-4-2 04:22:31

Learning Object Relation Graph and Tentative Policy for Visual Navigation, to learn informative visual representation and robust navigation policy. Aiming to improve these two components, this paper proposes three complementary techniques, object relation graph (ORG), trial-driven imitation learning (IL), and a memory-augmented tentative policy network (TPN). ORG improves

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