2 citations · 2 across the 1 of their papers we have counts for
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Random Walks for Adversarial Meshes
Amir Belder, Gal Yefet, Ran Ben Izhak +1
A polygonal mesh is the most-commonly used representation of surfaces in computer graphics. Therefore, it is not surprising that a number of mesh classification networks have recen…
AttWalk: Attentive Cross-Walks for Deep Mesh Analysis
Ran Ben Izhak, Alon Lahav, Ayellet Tal
Mesh representation by random walks has been shown to benefit deep learning. Randomness is indeed a powerful concept. However, it comes with a price: some walks might wander around…
Visual Navigation with Spatial Attention
Bar Mayo, Tamir Hazan, Ayellet Tal
This work focuses on object goal visual navigation, aiming at finding the location of an object from a given class, where in each step the agent is provided with an egocentric RGB…
MeshWalker: Deep Mesh Understanding by Random Walks
Alon Lahav, Ayellet Tal
Most attempts to represent 3D shapes for deep learning have focused on volumetric grids, multi-view images and point clouds. In this paper we look at the most popular representatio…
Color Visual Illusions: A Statistics-based Computational Model
Elad Hirsch, Ayellet Tal
Visual illusions may be explained by the likelihood of patches in real-world images, as argued by input-driven paradigms in Neuro-Science. However, neither the data nor the tools e…
Breaking the cycle -- Colleagues are all you need
Ori Nizan, Ayellet Tal
This paper proposes a novel approach to performing image-to-image translation between unpaired domains. Rather than relying on a cycle constraint, our method takes advantage of col…