Výsledky vyhledávání - "Winter , Daniel"

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ObjectDrop: Bootstrapping Counterfactuals for Photorealistic Object Removal and Insertion

Autor: Winter, Daniel, Cohen, Matan, Fruchter, Shlomi, Pritch, Yael, Rav-Acha, Alex, Hoshen, Yedid

Diffusion models have revolutionized image editing but often generate images that violate physical laws, particularly the effects of objects on the scene, e.g., occlusions, shadows, and reflections. By analyzing the limitations of self-supervised app

Externí odkaz: http://arxiv.org/abs/2403.18818

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Classifying Nodes in Graphs without GNNs

Autor: Winter, Daniel, Cohen, Niv, Hoshen, Yedid

Graph neural networks (GNNs) are the dominant paradigm for classifying nodes in a graph, but they have several undesirable attributes stemming from their message passing architecture. Recently, distillation methods succeeded in eliminating the use of

Externí odkaz: http://arxiv.org/abs/2402.05934

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