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pro vyhledávání: '"Sundaram, Shobhita"'
Autor:
Sundaram, Shobhita, Fu, Stephanie, Muttenthaler, Lukas, Tamir, Netanel Y., Chai, Lucy, Kornblith, Simon, Darrell, Trevor, Isola, Phillip
Humans judge perceptual similarity according to diverse visual attributes, including scene layout, subject location, and camera pose. Existing vision models understand a wide range of semantic abstractions but improperly weigh these attributes and th
Externí odkaz:
http://arxiv.org/abs/2410.10817
Autor:
Fu, Stephanie, Tamir, Netanel, Sundaram, Shobhita, Chai, Lucy, Zhang, Richard, Dekel, Tali, Isola, Phillip
Current perceptual similarity metrics operate at the level of pixels and patches. These metrics compare images in terms of their low-level colors and textures, but fail to capture mid-level similarities and differences in image layout, object pose, a
Externí odkaz:
http://arxiv.org/abs/2306.09344
Autor:
Villalobos, Kimberly, Štih, Vilim, Ahmadinejad, Amineh, Sundaram, Shobhita, Dozier, Jamell, Francl, Andrew, Azevedo, Frederico, Sasaki, Tomotake, Boix, Xavier
Publikováno v:
Neural Computation 33 (2021) 2511-2549
The insideness problem is an aspect of image segmentation that consists of determining which pixels are inside and outside a region. Deep Neural Networks (DNNs) excel in segmentation benchmarks, but it is unclear if they have the ability to solve the
Externí odkaz:
http://arxiv.org/abs/2201.10664
Symmetry is omnipresent in nature and perceived by the visual system of many species, as it facilitates detecting ecologically important classes of objects in our environment. Symmetry perception requires abstraction of long-range spatial dependencie
Externí odkaz:
http://arxiv.org/abs/2112.04162
Autor:
Sundaram, Shobhita, Hulkund, Neha
A common problem in computer vision -- particularly in medical applications -- is a lack of sufficiently diverse, large sets of training data. These datasets often suffer from severe class imbalance. As a result, networks often overfit and are unable
Externí odkaz:
http://arxiv.org/abs/2107.02970
Autor:
Sundaram, Shobhita1 (AUTHOR) shobhita@mit.edu, Sinha, Darius2 (AUTHOR), Groth, Matthew1 (AUTHOR), Sasaki, Tomotake3 (AUTHOR), Boix, Xavier1 (AUTHOR) xboix@mit.edu
Publikováno v:
Scientific Reports. 12/3/2022, Vol. 12 Issue 1, p1-16. 16p.
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